Systems and methods for tail-specific parameter computation

ABSTRACT

A device for tail-specific parameter computation includes a memory, a network interface, and a processor. The memory is configured to store a tail-specific aircraft performance model for a first aircraft of an aircraft type. The tail-specific aircraft performance model is based on historical flight data of the first aircraft and a nominal aircraft performance model associated with a second aircraft of the aircraft type. The network interface is configured to receive flight data from a databus of the first aircraft. The processor is configured to generate, based at least in part on the flight data and the tail-specific aircraft performance model, a recommended cost index and a recommended cruise altitude. The processor is also configured to provide the recommended cost index and the recommended cruise altitude to a display device.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority from and is a continuation ofpending U.S. patent application Ser. No. 16/284,477 entitled “SYSTEMSAND METHODS FOR TAIL-SPECIFIC PARAMETER COMPUTATION,” filed Feb. 25,2019, the contents of which are incorporated herein by reference intheir entirety.

FIELD OF THE DISCLOSURE

The present disclosure is generally related to systems and methods fordetermining tail-specific parameters.

BACKGROUND

Aircraft pilots typically have access to a cost index determined by anaircraft operator. The cost index is intended to reflect that aircraftoperator's time and fuel-related costs associated with a flight. Forexample, a lower cost index typically corresponds to reduced fuelconsumption, while a higher cost index corresponds to flying faster andincreased fuel consumption. A pilot provides the cost index (e.g., atarget cost index) to a flight management system of the aircraft. Theflight management system recommends, based on a nominal aircraftperformance model, a speed corresponding to the target cost index. Theperformance of an individual aircraft can differ from the nominalaircraft performance model for various reasons (e.g., manufacturingdifferences, changes over time, etc.) thereby causing an effective costindex of that aircraft (corresponding to the recommended speed) todiffer from the target cost index. As a result, the aircraft operator isunable to achieve the desired balance between the time-related costs andthe fuel-related costs.

SUMMARY

In a particular implementation, a device for tail-specific parametercomputation includes a memory, a network interface, and a processor. Thememory is configured to store a tail-specific aircraft performance modelfor a first aircraft of an aircraft type. The tail-specific aircraftperformance model is based on historical flight data of the firstaircraft and a nominal aircraft performance model associated with asecond aircraft of the aircraft type. The network interface isconfigured to receive flight data from a databus of the first aircraft.The processor is configured to generate, based at least in part on theflight data and the tail-specific aircraft performance model, arecommended cost index and a recommended cruise altitude. The processoris also configured to provide the recommended cost index and therecommended cruise altitude to a display device.

In another particular implementation, a method for tail-specificparameter computation includes receiving, at a device, flight data froma databus of a first aircraft of an aircraft type. The method alsoincludes generating, based at least in part on the flight data and atail-specific aircraft performance model, a recommended cost index and arecommended cruise altitude. The tail-specific aircraft performancemodel is based on historical flight data of the first aircraft and anominal aircraft performance model associated with a second aircraft ofthe aircraft type. The method further includes providing the recommendedcost index and the recommended cruise altitude from the device to adisplay device of the first aircraft.

In another particular implementation, a computer-readable storage devicestores instructions that, when executed by a processor, cause theprocessor to perform operations including receiving flight data from adatabus of a first aircraft of an aircraft type. The operations alsoinclude generating, based at least in part on the flight data and atail-specific aircraft performance model, a recommended cost index and arecommended cruise altitude. The tail-specific aircraft performancemodel is based on historical flight data of the first aircraft and anominal aircraft performance model associated with a second aircraft ofthe aircraft type. The operations further include providing therecommended cost index and the recommended cruise altitude to a displaydevice of the first aircraft.

The features, functions, and advantages described herein can be achievedindependently in various implementations or may be combined in yet otherimplementations, further details of which can be found with reference tothe following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram that illustrates a system for tail-specificparameter computation;

FIG. 2 is a diagram that illustrates aspects of a flight data parser anda memory of the system of FIG. 1;

FIG. 3 is a diagram that illustrates additional aspects of the flightdata parser and the memory of the system of FIG. 1;

FIG. 4 is a diagram that illustrates aspects of a tail-specificparameter generator and the memory of the system of FIG. 1;

FIG. 5 is a diagram that illustrates aspects of a recommendationgenerator and the memory of the system of FIG. 1;

FIG. 6 is a diagram that illustrates aspects of a graphical userinterface generated by the system of FIG. 1;

FIG. 7 is a flow chart of an example of a method of tail-specificparameter computation; and

FIG. 8 is a block diagram of an aircraft configured to support aspectsof computer-implemented methods and computer-executable programinstructions (or code) according to the present disclosure.

DETAILED DESCRIPTION

Implementations described herein are directed to systems and methods fortail-specific parameter computation. A particular aircraft includes anon-board computing device that has access to a nominal aircraftperformance model. The nominal aircraft performance model is associatedwith an aircraft type of the particular aircraft. For example, thenominal aircraft performance model is representative of a predictedaverage performance for aircrafts of the aircraft type. In someexamples, the nominal aircraft performance model represents aircraftperformance of a representative aircraft (e.g., a newly manufacturedaircraft) of the aircraft type. To illustrate, the representativeaircraft is assumed by the manufacturer to represent performance of allaircraft of the aircraft type. In practice, performance of individualaircraft can vary considerably from aircraft to aircraft.

A flight data parser has access to historical flight data of theparticular aircraft. In some examples, the flight data parser isintegrated into the particular aircraft. In alternative examples, anoff-board device (e.g., a ground-based device) includes the flight dataparser. The flight data parser updates the historical flight data byremoving entries that correspond to outliers, sorting entries of thehistorical flight data, or a combination thereof.

A tail-specific parameter generator generates tail-specific parametersbased on the historical flight data of the particular aircraft. As usedherein, “tail-specific” refers to “specific to the particular aircraft.”To illustrate, the tail-specific parameters are specific to (or relatedto) the particular aircraft. In some examples, the tail-specificparameter generator is integrated into the particular aircraft. Inalternative examples, an off-board device (e.g., a ground-based device)includes the tail-specific parameter generator. The tail-specificparameter generator generates tail-specific parameters based on thehistorical flight data of the particular aircraft. The tail-specificparameters represent aircraft performance specific to the particularaircraft. The tail-specific parameter generator generates atail-specific aircraft performance model by updating the nominalaircraft performance model based on the tail-specific parameters.

The on-board computing device includes a recommendation generator. Therecommendation generator receives flight data from a databus of theparticular aircraft and generates (e.g., in real-time) a recommendedcost index based on the flight data and the tail-specific aircraftperformance model. In some examples, the recommended cost indexcorresponds to an effective cost index of the particular aircraft thatis the same as the target cost index. In some examples, the on-boardcomputing device also determines a recommended cruise altitude, arecommended speed, or both. For example, the on-board computing devicedetermines (based on the nominal aircraft performance model) therecommended cruise altitude, the recommended speed, or both,corresponding to the recommended cost index. In some examples, therecommended cost index, the recommended speed, the recommended cruisealtitude, or a combination thereof, reduce (e.g., minimize) an operatingcost of the particular aircraft. For example, the recommended cost indexcorresponds to an effective cost index of the aircraft that balancestime-related costs and fuel-related costs such that the overalloperating cost is reduced (e.g., minimized).

As used herein, various terminology is used for the purpose ofdescribing particular implementations only and is not intended to belimiting. For example, the singular forms “a,” “an,” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. Further, the terms “comprise,” “comprises,” and“comprising” are used interchangeably with “include,” “includes,” or“including.” Additionally, the term “wherein” is used interchangeablywith the term “where.” As used herein, “exemplary” indicates an example,an implementation, and/or an aspect, and should not be construed aslimiting or as indicating a preference or a preferred implementation. Asused herein, an ordinal term (e.g., “first,” “second,” “third,” etc.)used to modify an element, such as a structure, a component, anoperation, etc., does not by itself indicate any priority or order ofthe element with respect to another element, but rather merelydistinguishes the element from another element having a same name (butfor use of the ordinal term). As used herein, the term “set” refers to agrouping of one or more elements, and the term “plurality” refers tomultiple elements.

Referring to FIG. 1, a system 100 for tail-specific parametercomputation is shown. The system 100 includes an aircraft 108. Theaircraft 108 includes an on-board computing device 102, a databus 140,one or more sensors 142, a display device 144, or a combination thereof.The on-board computing device 102 includes a processor 170, a memory122, a network interface 130 (e.g., a first network interface), anetwork interface 132 (e.g., a second network interface), or acombination thereof. In a particular aspect, the on-board computingdevice 102 includes or corresponds to an aircraft integration device(AID), a flight management system, or both. In a particular aspect, thememory 122, the network interface 130, the processor 170, arecommendation generator 176, the on-board computing device 102, or acombination thereof, are integrated into a portable Electronic FlightBag (EFB) computer. In a particular aspect an EFB computer includes atablet, a mobile device, a communication device, a computing device, ora combination thereof.

It should be noted that in the following description, various functionsperformed by the system 100 of FIG. 1 are described as being performedby certain components or modules. However, this division of componentsand modules is for illustration only. In an alternate aspect, a functiondescribed herein as performed by a particular component or module isdivided amongst multiple components or modules. Moreover, in analternate aspect, two or more components or modules of FIG. 1 areintegrated into a single component or module. In a particular aspect,one or more functions described herein as performed by the on-boardcomputing device 102 are divided amongst multiple devices (e.g., theon-board computing device 102, an AID, a flight management system, acentral server, a distributed system, or any combination thereof). Eachcomponent or module illustrated in FIG. 1 may be implemented usinghardware (e.g., a field-programmable gate array (FPGA) device, anapplication-specific integrated circuit (ASIC), a digital signalprocessor (DSP), a controller, etc.), software (e.g., instructionsexecutable by a processor), or any combination thereof.

The memory 122 includes volatile memory devices (e.g., random accessmemory (RAM) devices), nonvolatile memory devices (e.g., read-onlymemory (ROM) devices, programmable read-only memory, and flash memory),or both. In a particular aspect, the memory 122 includes one or moreapplications (e.g., instructions) executable by the processor 170 toinitiate, control, or perform one or more operations described herein.In an illustrative example, a computer-readable storage device (e.g.,the memory 122) includes instructions that, when executed by theprocessor 170, cause the processor 170 to initiate, perform, or controloperations described herein. In a particular aspect, the memory 122 isconfigured to store instructions 179 that are executable by theprocessor 170 to perform one or more operations described herein.

In a particular aspect, the memory 122 is configured to store a nominalaircraft performance model 185. The nominal aircraft performance model185 is associated with an aircraft type 187 of the aircraft 108. Forexample, the nominal aircraft performance model 185 is representative ofa predicted average performance for aircrafts of the aircraft type 187.In a particular aspect, the nominal aircraft performance model 185 isgenerated by a manufacturer of the aircraft 108. The off-board device162 (or another device) generates the nominal aircraft performance model185 based on aircraft performance of a representative aircraft (e.g., anewly manufactured aircraft) of the aircraft type 187. In a particularaspect, the nominal aircraft performance model 185 is based on anestimated gross weight (e.g., an assumed gross weight) of therepresentative aircraft.

The sensors 142 are configured to provide flight data 105 to the databus140. The flight data 105 indicates measurements performed by the sensors142, as further described with reference to FIG. 5. The on-boardcomputing device 102 is configured to receive the flight data 105 viathe network interface 130 from the databus 140. In a particular aspect,the on-board computing device 102 (e.g., an aircraft integration device)obtains the flight data 105 as one or more of the sensors 142 providethe flight data 105 via the databus 140 to a digital flight datarecorder. For example, the on-board computing device 102 obtains, at afirst time, a first portion of the flight data 105 from a first sensorof the sensors 142. The on-board computing device 102 obtains, at asecond time, a second portion of the flight data 105 from a secondsensor of the sensors 142. To illustrate, the first sensor provides thefirst portion of the flight data 105 at first time intervals, inresponse to detecting a first event, or both. The second sensor providesthe second portion of the flight data 105 at second time intervals, inresponse to detecting a second event, or both.

The network interface 132 is configured to communicate, via an off-boardnetwork 160, with an off-board device 162 (e.g., a ground-based device).The off-board network 160 includes a wired network, a wireless network,or both. The off-board network 160 includes one or more of a local areanetwork (LAN), a wide area network (WAN), a cellular network, and asatellite network.

The processor 170 includes a flight data parser 172, a tail-specificparameter generator 174, the recommendation generator 176, or acombination thereof. The flight data parser 172 is configured totranslate historical flight data 107 of the aircraft 108 into a formatreadable by the tail-specific parameter generator 174, remove outliersfrom the historical flight data 107, or both, as further described withreference to FIG. 3. The tail-specific parameter generator 174 isconfigured to generate tail-specific parameters 141 based on thehistorical flight data 107, as further described with reference to FIG.4. For example, the tail-specific parameters 141 represent aircraftperformance of the aircraft 108. In a particular aspect, thetail-specific parameter generator 174 generates a tail-specific aircraftperformance model 181 based on the tail-specific parameters 141, asdescribed with reference to FIG. 4. For example, the tail-specificparameter generator 174 generates the tail-specific aircraft performancemodel 181 by updating the nominal aircraft performance model 185 basedon the tail-specific parameters 141.

In a particular aspect, the flight data parser 172, the tail-specificparameter generator 174, or both are integrated into the off-boarddevice 162. In this aspect, the off-board device 162 includes a memoryconfigured to store data used (or generated) by the flight data parser172, the tail-specific parameter generator 174, or both. The on-boardcomputing device 102 receives the tail-specific parameters 141, thetail-specific aircraft performance model 181, or both, via the off-boardnetwork 160, from the off-board device 162. The on-board computingdevice 102 stores the tail-specific parameters 141, the tail-specificaircraft performance model 181, or a combination thereof, in the memory122.

The recommendation generator 176 is configured to generate arecommendation 191 based on a target cost index 189, the flight data105, and the tail-specific aircraft performance model 181, as furtherdescribed with reference to FIG. 5. For example, the recommendation 191includes a recommended cost index 193. In a particular aspect, therecommendation generator 176 is configured to generate a recommendedcruise altitude 195, a recommended speed 197, or a combination thereof,as further described with reference to FIG. 5. In a particular aspect,the recommendation generator 176 performs one or more operationsdescribed with reference to the flight data parser 172. For example, therecommendation generator 176 determines whether the flight data 105corresponds to an outlier and generates the recommendation 191 inresponse to determining that the flight data 105 does not correspond toan outlier.

During a first stage of operation, the flight data parser 172 accessesthe historical flight data 107 of the aircraft 108. For example, thefirst stage of operation corresponds to pre-flight preparation,post-flight updates, or both. In other examples, the first stage ofoperation occurs during a flight of the aircraft 108. In a particularaspect, a user (e.g., an information technology (IT) administrator)provides (subsequent to a first flight of the aircraft 108, prior to asecond flight of the aircraft 108, or both) a user input indicating anaircraft identifier (ID) of the aircraft 108 to the flight data parser172. The flight data parser 172 accesses the historical flight data 107of the aircraft 108 in response to receiving the user input indicatingthe aircraft ID. In a particular aspect, a user provides a user inputindicating an aircraft ID of the aircraft 108 to the flight data parser172 if the historical flight data 107 does not indicate the aircraft ID.The flight data parser 172 updates the historical flight data 107, asfurther described with reference to FIGS. 2-3. For example, the flightdata parser 172 applies a gravitational variation adjustment to grossweight values indicated by the historical flight data 107, as furtherdescribed with reference to FIG. 2. As another example, the flight dataparser 172 identifies entries of the historical flight data 107 thatcorrespond to outliers and updates the historical flight data 107 byremoving the identified entries, as further described with reference toFIG. 3.

The tail-specific parameter generator 174 determines the tail-specificparameters 141 based on the historical flight data 107, as furtherdescribed with reference to FIG. 4. In a particular aspect, thetail-specific parameter generator 174 determines the tail-specificparameters 141 in response to receiving a user input from a user (e.g.,an IT administrator), receiving the historical flight data 107 from theflight data parser 172, receiving an update to the historical flightdata 107 from the flight data parser 172, receiving a notification fromthe flight data parser 172 indicating that the historical flight data107 is updated, or a combination thereof.

In a particular aspect, the tail-specific parameter generator 174generates (or updates) the tail-specific aircraft performance model 181based on the tail-specific parameters 141, as described with referenceto FIG. 4. For example, the tail-specific parameter generator 174 hasaccess to the nominal aircraft performance model 185 associated with theaircraft type 187 of the aircraft 108. The tail-specific parametergenerator 174 generates the tail-specific aircraft performance model 181by updating the nominal aircraft performance model 185 based on thetail-specific parameters 141. The nominal aircraft performance model 185represents aircraft performance corresponding to a representativeaircraft of the aircraft type 187. The tail-specific aircraftperformance model 181 represents aircraft performance of the aircraft108.

In a particular aspect, the off-board device 162 includes the flightdata parser 172, the tail-specific parameter generator 174, or both. Theon-board computing device 102 receives the tail-specific parameters 141,the tail-specific aircraft performance model 181, or both, from theoff-board device 162. For example, a user (e.g., a pilot) provides,prior to a flight, a user input to the on-board computing device 102 torequest a flight plan from the off-board device 162. The off-boarddevice 162, in response to receiving the request for the flight planfrom the on-board computing device 102 and determining that the requestindicates the aircraft ID of the aircraft 108, sends the flight plan,the tail-specific parameters 141, the tail-specific aircraft performancemodel 181, or a combination thereof, to the on-board computing device102.

It should be understood that the on-board computing device 102 isprovided as an illustrative example. In some examples, the on-boardcomputing device 102 corresponds to a mobile device (e.g., a tablet, acommunication device, a computing device, or a combination thereof) thatcan be on-board the aircraft 108 at various times and off-board theaircraft 108 at other times. In a particular aspect, the pilot uses themobile device to send the request for the flight plan prior to boardingthe aircraft 108.

The on-board computing device 102 receives the target cost index 189.The target cost index 189 corresponds to a configuration setting, a userinput, default data, or a combination thereof. In a particular aspect,the on-board computing device 102 receives the target cost index 189from the off-board device 162, from a user (e.g., an IT administrator ora pilot), or a combination thereof. For example, a pilot provides thetarget cost index 189 to the on-board computing device 102 prior to aflight. In a particular aspect, the on-board computing device 102determines, based on the nominal aircraft performance model 185, a planaltitude 155 (e.g., a plan optimum altitude or flight level), a planspeed 157, a plan fuel mileage 159, or a combination thereof,corresponding to the target cost index 189.

During a second stage of operation, the sensors 142 provide the flightdata 105 to the databus 140. For example, the second stage of operationoccurs during a flight of the aircraft 108. In some examples, the secondstage of operation occurs subsequent to a first flight of the aircraft108, prior to a second flight of the aircraft 108, or both. The sensors142 provide the flight data 105 to the databus 140 at a particular timeinterval, in response to detecting an event, in response to receiving arequest from a component of the aircraft 108, or a combination thereof.In a particular aspect, the flight data 105 indicates measurementsperformed by the sensors 142 during the flight.

The recommendation generator 176 generates the recommendation 191 basedon the flight data 105, the tail-specific parameters 141, thetail-specific aircraft performance model 181, the target cost index 189,or a combination thereof, as further described with reference to FIG. 5.In a particular aspect, the recommendation generator 176 generates therecommendation 191 in response to receiving a user input from a user(e.g., a pilot), detecting cruise flight at a particular altitude of theaircraft 108, receiving the flight data 105, detecting a change in theflight data 105, or a combination thereof. The recommendation 191includes the recommended cost index 193. In some aspects, therecommendation generator 176 determines the recommended cruise altitude195, the recommended speed 197, or a combination thereof, correspondingto the recommended cost index 193, as further described with referenceto FIG. 5.

The recommendation generator 176 generates a GUI 163 indicating therecommendation 191. For example, the GUI 163 indicates the recommendedcost index 193, the recommended cruise altitude 195, the recommendedspeed 197, or a combination thereof. The recommendation generator 176provides the GUI 163 to the display device 144. In some aspects, the GUI163 indicates the target cost index 189, the plan altitude 155, the planspeed 157, the plan fuel mileage 159, or a combination thereof. Theflight data 105 indicates a detected speed, a detected altitude, adetected fuel mileage of the aircraft 108, or a combination thereof. Insome aspects, the GUI 163 indicates the detected speed, the detectedaltitude, the detected fuel mileage, or a combination thereof.

In a particular aspect, the on-board computing device 102 receives auser input indicating a selected cost index 153, a selected speed 165, aselected altitude 167, or a combination thereof. For example, a pilotselects the selected cost index 153, the selected altitude 167, theselected speed 165, or a combination thereof. In a particular aspect,the selected cost index 153 is distinct from the target cost index 189,the recommended cost index 193, or both. In some examples, the on-boardcomputing device 102 automatically (e.g., independently of user input)sets the selected cost index 153 to the recommended cost index 193, theselected speed 165 to the recommended speed 197, the selected altitude167 to the recommended cruise altitude 195, or a combination thereof.

In a particular aspect, the on-board computing device 102 determines theselected speed 165 corresponding to the selected cost index 153. Forexample, the on-board computing device 102 has access to a speedcalculator (e.g., an economy (Econ) cruise speed table) that maps theselected cost index 153 and a wind component along track derived from agross weight, a pressure ratio, a temperature ratio, or a combinationthereof to the selected speed 165. The flight data 105 indicates theground speed, the true airspeed, the gross weight, the pressure ratio,the temperature ratio, or a combination thereof, as further describedwith reference to FIG. 5.

In a particular aspect, the GUI 163 indicates the selected speed 165,the selected altitude 167, the selected cost index 153, or a combinationthereof. The on-board computing device 102 (or another component of theaircraft 108) generates one or more control commands to update analtitude of the aircraft 108 to the selected altitude 167, a speed ofthe aircraft 108 to the selected speed 165, or both.

During a third stage of operation, the flight data parser 172 adds theflight data 105 to the historical flight data 107 in response todetermining that the flight data 105 satisfies a filter criterion. Forexample, the third stage of operation corresponds to post-flightmaintenance. In some examples, the third stage of operation occursduring the flight of the aircraft 108. It should be understood thatthree stages of operation are provided as an illustrative example. Insome aspects, the operations described herein are performed in fewerthan three stages or more than three stages. In a particularimplementation, the recommendation generator 176 provides the flightdata 105 to the off-board device 162. For example, the recommendationgenerator 176 provides the flight data 105 to the off-board device 162in response to determining that the on-board computing device 102 iswithin a communication range of the off-board device 162, determiningthat the aircraft 108 has a particular status (e.g., landed), receivinga user input indicating that the flight data 105 is to be provided tothe off-board device 162, receiving a request from the off-board device162, or a combination thereof. In this example, the flight data parser172 of the off-board device 162 adds the flight data 105 to thehistorical flight data 107 in response to determining that the flightdata 105 satisfies the filter criterion. The tail-specific parametergenerator 174 uses the updated historical flight data 107 to generate(or update) the tail-specific aircraft performance model 181 for use insubsequent flights of the aircraft 108.

The system 100 thus enables computation of the tail-specific parameters141 that are based on the historical flight data 107 of the aircraft108. The tail-specific parameters 141 are used to determine therecommended cost index 193. In some examples, the recommended cost index193 corresponds to an effective cost index of the aircraft 108 thatachieves the target time/fuel cost defined by the target cost index 189.In some aspects, the recommended cost index 193, the recommended cruisealtitude 195, the recommended speed 197, or a combination thereof,reduce (e.g., minimize) operational costs of the aircraft 108 duringflight.

FIGS. 2-3 illustrate aspects of the flight data parser 172 and thememory 122. FIG. 2 illustrates update of a gross weight based on agravitational variation adjustment and determination of parameter valuesbased on historical data. FIG. 3 illustrates filtration of thehistorical data based on a filter criterion.

Referring to FIG. 2, a diagram 200 illustrates aspects of the flightdata parser 172 and the memory 122. The flight data parser 172 hasaccess to the historical flight data 107 of the aircraft 108 of FIG. 1.

The historical flight data 107 includes a plurality of entries 290. Eachof the entries 290 corresponds to a particular instance of the flightdata 105 of FIG. 1. For example, the entries 290 include an entry 242that corresponds to the flight data 105 received, during a particularflight, by the on-board computing device 102 at a first time from thedatabus 140, generated by the sensors 142 during a first time interval,or both. In a particular aspect, the entries 290 include a second entrythat corresponds to the flight data 105 received by the on-boardcomputing device 102 at a second time from the databus 140, generated bythe sensors 142 during a second time interval, or both.

The entry 242 indicates speed information (e.g., a Mach number 202, aground speed 220, or both), location information (e.g., a pressurealtitude 206, a latitude 216, or both), attitude information (e.g., aleft angle of attack (AOA) 210, a right AOA 212, pitch attitude (e.g.,left pitch 214), a heading 218, or a combination thereof), ambientenvironment conditions (e.g., a total air temperature 208), weightinformation (e.g., a gross weight 204), fuel information (e.g., a fuelflow 222, a fuel weight 224, or both), settings (e.g., a stabilizer trimsetting 226), or a combination thereof. In a particular aspect, thehistorical flight data 107 corresponds to a comma separated values (CSV)file and each line of the CSV file corresponds to an entry of thehistorical flight data 107.

In a particular aspect, the Mach number 202, the gross weight 204, thepressure altitude 206, the total air temperature 208, the left AOA 210,the right AOA 212, the left pitch 214, the latitude 216, the heading218, the ground speed 220, the fuel flow 222, the fuel weight 224, thestabilizer trim setting 226, or a combination thereof, are detected bythe sensors 142 during the particular flight of the aircraft 108. Forexample, the Mach number 202 corresponds to a detected Mach number ofthe aircraft 108 during the particular flight. The gross weight 204corresponds to a reported gross weight of the aircraft 108 during theparticular flight. The pressure altitude 206 corresponds to a detectedpressure altitude of the aircraft 108 during the particular flight. Thetotal air temperature 208 corresponds to a detected air temperatureoutside the aircraft 108 during the particular flight. The left AOA 210corresponds to a detected left AOA of the aircraft 108 during theparticular flight. The right AOA 212 corresponds to a detected right AOAof the aircraft 108 during the particular flight. The left pitch 214corresponds to a detected left pitch of the aircraft 108 during theparticular flight. The latitude 216 corresponds to detected latitude ofthe aircraft 108 during the particular flight. The heading 218corresponds to a detected heading of the aircraft 108 during theparticular flight. The ground speed 220 corresponds to a detected groundspeed of the aircraft 108 during the particular flight. The fuel flow222 corresponds to a detected fuel flow of the aircraft 108 during theparticular flight. The fuel weight 224 corresponds to a detected fuelweight of the aircraft 108 during the particular flight. For example,the sensors 142 includes a fuel volume sensor that detects a volume offuel of the aircraft 108 and determines the fuel weight 224 based on thevolume of fuel. The stabilizer trim setting 226 corresponds to adetected stabilizer trim setting of the aircraft 108 during theparticular flight.

In a particular aspect, each of the sensors 142 of FIG. 1 generates atime-series of values. For example, a first sensor of the sensors 142generates a first time-series of values (e.g., Mach numbers) and asecond sensor of the sensors 142 generates a second time-series ofvalues (e.g., total air temperature measurements). In a particularaspect, the first sensor generates the first time-series of values(e.g., at two second intervals) asynchronously with the secondtime-series of values (e.g., at ten second intervals). For example, thefirst sensor generates a first Mach number at time t1 (e.g., 10:00:01),a second Mach number at time t2 (e.g., 10:00:03), a third Mach number attime t3 (e.g., 10:00:05), a fourth Mach number at time t4 (e.g.,10:00:07), a fifth Mach number at time t5 (e.g., 10:00:09), and so on.The second sensor generates a first total air temperature at time t11(e.g., 10:00:02) and a second total air temperature at time t12 (e.g.,10:00:12).

In a particular aspect, the flight data parser 172 uses aggregation orbinning to determine flight data values corresponding to a commontime-series (e.g., at five second intervals). For example, the flightdata parser 172 determines values for the entry 242 corresponding to thefirst time interval (e.g., 10:00:03-10:00:08). To illustrate, the flightdata parser 172 determines a first value (e.g., the Mach number 202)based on the second Mach number for time t2 (e.g., 10:00:03), the thirdMach number for time t3 (e.g., 10:00:05), the fourth Mach number fortime t4 (e.g., 10:00:07), or a combination thereof. In a particularaspect, the first value (e.g., the Mach number 202) is based on anaverage of the second Mach number for time t2 (e.g., 10:00:03), thethird Mach number for time t3 (e.g., 10:00:05), the fourth Mach numberfor time t4 (e.g., 10:00:07), or a combination thereof.

In a particular example, the flight data parser 172 determines a secondvalue (e.g., the total air temperature 208) based on the first total airtemperature for time t11 (e.g., 10: 00:02), the second total airtemperature at time t12 (e.g., 10:00:12), or both. For example, theflight data parser 172 designates the first total air temperature fortime t11 as the second value (e.g., the total air temperature 208) inresponse to determining that the first total air temperature is the mostrecently received value from the second sensor prior to the first timeinterval (e.g., 10:00:03-10:00:08). As another example, the flight dataparser 172 determines the second value (e.g., the total air temperature208) based on an average of the first total air temperature for time t11(e.g., 10:00:02) and the second total air temperature at time t12 (e.g.,10:00:12).

In a particular aspect, the entry 242 indicates a data collection time292 corresponding to the first time interval. For example, the datacollection time 292 includes a first timestamp corresponding to abeginning (e.g., 10:00:03) of the first time interval, a secondtimestamp corresponding to an end (e.g., 10:00:08) of the first timeinterval, a third timestamp corresponding to a middle (e.g., 10:00:05)of the first time interval, or a combination thereof.

In a particular aspect, the gross weight 204 is based on user input, aconfiguration setting, default data, or a combination thereof. Forexample, a pilot provides user input indicating the gross weight 204(e.g., an initial gross weight) to the on-board computing device 102.The gross weight 204 (e.g., the initial gross weight) is based on acount of passengers, a count of checked baggage, or a combinationthereof.

The flight data parser 172 determines one or more values based on thehistorical flight data 107. For example, the flight data parser 172determines an AOA 228 (e.g., a detected angle of attack) based on theleft AOA 210, the right AOA 212, the left pitch 214, or a combinationthereof. In a particular aspect, the flight data parser 172 determinesthe AOA 228 based on the following Equation:

$\begin{matrix}{{AOA} = {5 + \frac{{AOA_{L}} + {AOA_{R}} + {Pitch}_{L}}{3}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

where AOA corresponds to AOA 228, AOA_(L) corresponds to the left AOA210, AOA_(R) corresponds to the right AOA 212, and Pitch_(L) correspondsto the left pitch 214. It should be understood that calculations basedon the left pitch 214 are provided as illustrative examples. In otherexamples, the calculations are based on a right pitch of the aircraft108 instead of the left pitch 214.

In a particular aspect, the flight data parser 172 determines a staticair temperature 230 based on the Mach number 202 and the total airtemperature 208. For example, the flight data parser 172 determines thestatic air temperature 230 based on the following Equation:

$\begin{matrix}{{SAT} = {\frac{{TAT} + 273.15}{1 + {0.2M^{2}}} - 273.15}} & {{Equation}\mspace{20mu} 2}\end{matrix}$

where SAT corresponds to the static air temperature 230, TAT correspondsto the total air temperature 208, and M corresponds to the Mach number202.

In a particular aspect, the flight data parser 172 determines aninternational standard atmosphere (ISA) deviation 232 based on thestatic air temperature 230 and the pressure altitude 206. For example,the flight data parser 172 determines the ISA deviation 232 based on thefollowing Equation:

$\begin{matrix}{{{{\Delta\;{ISA}} = {{SAT} - \left( {15 - \frac{1.98\; h_{p}}{1000}} \right)}},{h_{p} \leq {36089\mspace{14mu}{ft}}}}{{{\Delta\;{ISA}} = {{SAT} - \left( {{- 5}{6.5}} \right)}},{h_{p} > {36089\mspace{14mu}{ft}}}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

where ΔISA corresponds to the ISA deviation 232, SAT corresponds to thestatic air temperature 230, and h_(p) corresponds to the pressurealtitude 206.

In a particular aspect, the flight data parser 172 determines atemperature ratio 234 based on the static air temperature 230. Forexample, the flight data parser 172 determines the temperature ratio 234based on the following Equation:

$\begin{matrix}{\theta = \frac{{SAT} + 273.15}{28{8.1}5}} & {{Equation}\mspace{14mu} 4}\end{matrix}$

where θ corresponds to the temperature ratio 234, and SAT corresponds tothe static air temperature 230.

In a particular aspect, the flight data parser 172 determines a pressureratio 236 based on the pressure altitude 206. For example, the flightdata parser 172 determines the pressure ratio 236 based on the followingEquation:

$\begin{matrix}{{{\delta = \left( \frac{{28{8.1}5} - {{0.0}019812\mspace{11mu} h_{p}}}{28{8.1}5} \right)^{{5.2}5588}},{h_{p} \leq {36089\mspace{14mu}{ft}}}}{{\delta = {{0.2}2336e^{(\frac{36089 - h_{p}}{2080{5.7}})}}},{h_{p} > {36089\mspace{14mu}{ft}}}}} & {{Equation}\mspace{20mu} 5}\end{matrix}$

where δ corresponds to the pressure ratio 236, h_(p) corresponds to thepressure altitude 206, and e corresponds to Euler's number (2.718281828. . . ).

In a particular aspect, the flight data parser 172 determines a trueairspeed 238 (e.g., a detected airspeed) based on the temperature ratio234 and the Mach number 202. For example, the flight data parser 172determines the true airspeed 238 based on the following Equation:

VTAS=661.475Mθ ^(0.5)  Equation 6

where VTAS corresponds to the true airspeed 238, M corresponds to theMach number 202, and θ corresponds to the temperature ratio 234.

In a particular aspect, the flight data parser 172 determines a grossweight over delta 240 based on the pressure ratio 236 and the grossweight 204. In a particular aspect, the gross weight over delta 240 isbased on the pressure ratio 236 and a gross weight 264. The flight dataparser 172 determines the gross weight 264 based on the gross weight204, as described herein. In a particular aspect, the flight data parser172 determines the gross weight over delta 240 based on the followingEquation:

$\begin{matrix}{{Wdel} = \frac{GW}{\left( {\delta \times 10^{ó}} \right)}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

where Wdel corresponds to the gross weight over delta 240 and θcorresponds to the pressure ratio 236. In a particular aspect, GWcorresponds to the gross weight 204. In an alternative aspect, GWcorresponds to the gross weight 264.

In a particular aspect, the flight data parser 172 determines a fuelmileage 282 based on the true airspeed 238 and the fuel flow 222. Forexample, the flight data parser 172 determines the fuel mileage 282based on the following Equation:

$\begin{matrix}{{FM} = \frac{VTAS}{FF}} & {{Equation}\mspace{20mu} 8}\end{matrix}$

where FM corresponds to the fuel mileage 282, VTAS corresponds to thetrue airspeed 238, and FF corresponds to the fuel flow 222.

In a particular aspect, the flight data parser 172 generates the grossweight 264 by adjusting the gross weight 204 based on a gravitationalvariation adjustment 267. The gravitational variation adjustment 267includes adjustments for gravitational variation with latitude,altitude, Coriolis force, and centrifugal force. For example, the flightdata parser 172 uses gravitational correction techniques to determine alatitude and altitude gravitational variation 253, a Coriolis andcentrifugal force gravitational variation 255, or a combination thereof.The latitude and altitude gravitational variation 253 representsgravitational acceleration at the latitude 216 and at the pressurealtitude 206. The Coriolis and centrifugal force gravitational variation255 represents gravitational acceleration caused by the Coriolis forceand the centrifugal force. The flight data parser 172 uses gravitationalcorrection techniques to determine the gravitational variationadjustment 267 based at least in part on the latitude and altitudegravitational variation 253, the Coriolis and centrifugal forcegravitational variation 255, or a combination thereof.

In a particular aspect, the flight data parser 172 determines the grossweight 264 based on the gross weight 204 and the gravitational variationadjustment 267 (e.g., the gross weight 264=the gross weight 204+thegravitational variation adjustment 267). The flight data parser 172updates the entry 242 to indicate the gross weight 264. The gross weight264 corresponds to the gross weight 204 adjusted (e.g., corrected) toaccount for gravitational variations (e.g., the gravitational variationadjustment 267).

Referring to FIG. 3, a diagram 300 illustrates aspects of the flightdata parser 172 and the memory 122. The flight data parser 172 isconfigured to filter the historical flight data 107 based on a filtercriterion 301, a filter criterion 303, or both. In a particular aspect,the filter criterion 301, the filter criterion 303, or both, correspondto user input, default data, a configuration setting, or a combinationthereof. In a particular aspect, the flight data parser 172 isconfigured to convert the historical flight data 107 from a first formatto a second format. For example, the first format is supported by thesensors 142 and the second format is supported by the tail-specificparameter generator 174.

In a particular aspect, the flight data parser 172 sorts the entries 290based on a difference between the pressure altitude 206 and a targetaltitude 315 (e.g., abs (pressure altitude 206—target altitude 315). Forexample, the flight data parser 172 determines a first sorting value ofthe entry 242 based on an absolute value of a difference between thepressure altitude 206 and the target altitude 315. The flight dataparser 172 determines a second sorting value of a second entry of theentries 290 based on an absolute value of a difference between apressure altitude of the second entry and the target altitude 315. Theflight data parser 172 sorts the entries 290 based on an ascending orderor a descending order. For example, the flight data parser 172 sorts theentries 290 so that the entry 242 has a first sorted position in theentries 290 and the second entry has a second sorted position in theentries 290. In a particular aspect, the first sorted position is priorto the second sorted position in cases where the first sorting value isless than the second sorting value. In an alternative aspect, the firstsorted position is subsequent to the second sorted position in caseswhere the first sorting value is less than the second sorting value.

In a particular aspect, the target altitude 315 (e.g., a target pressurealtitude) corresponds to the selected altitude 167 of FIG. 1. In aparticular aspect, the filter criterion 301, the filter criterion 303,the target altitude 315 (e.g., 31080 ft), an altitude threshold 353(e.g., 24900 ft), a gross weight threshold 355 (e.g., 100000 pounds(lb)), an altitude change threshold 357 (e.g., 1 ft), a fuel flow changethreshold 359 (e.g., 50 lb/hour (hr)), an AOA change threshold 361(e.g., 0.05 degrees), or a combination thereof, correspond to userinput, configuration data, default data, or a combination thereof.

The filter criterion 301 indicates overall threshold conditions (e.g.,the altitude threshold 353, the gross weight threshold 355, or both),change threshold conditions (e.g., the altitude change threshold 357,the fuel flow change threshold 359, the AOA change threshold 361, or acombination thereof), or a combination thereof. For example, the flightdata parser 172 determines whether the entry 242 satisfies the filtercriterion 301 based on determining whether the entry 242 satisfy theoverall threshold conditions, the change threshold conditions, or acombination thereof. To illustrate, the flight data parser 172determines that the entry 242 fails to satisfy the filter criterion 301in response to determining that the pressure altitude 206 fails tosatisfy (e.g., is less than) the altitude threshold 353 (e.g., 24900ft), that the gross weight 264 (e.g., a reported gross weight) fails tosatisfy (e.g., is less than) the gross weight threshold 355 (e.g.,100000 pounds (lb)), or both. In a particular aspect, the flight dataparser 172 uses the overall threshold conditions (e.g., the altitudethreshold 353, the gross weight threshold 355, or both) to filter outentries that correspond to flight data collected when the aircraft 108is experiencing flight conditions (e.g., too low, too light, ascending,descending, or a combination thereof) that are not consideredrepresentative of particular flight conditions (e.g., cruise) of theaircraft 108. In a particular aspect, the flight data parser 172determines parameter value changes between entries of the entries 290.For example, the entry 242 of the entries 290 is a next entry subsequentto a first entry of the entries 290. The first entry corresponds to afirst data collection time. The entry 242 corresponds to the datacollection time 292. The entry 242 is the next entry subsequent to thefirst entry in a sorted version of the entries 290. In a particularimplementation, the flight data parser 172 determines the parametervalue changes based on a first sorted version of the entries 290 wherethe entries 290 are sorted based on the data collection time. Forexample, the entry 242 is the next entry subsequent to the first entryin the first sorted version of the entries 290 because the datacollection time 292 is next and subsequent to the first data collectiontime. In a particular aspect, each of the first entry and the entry 242correspond to flight data collected during a single flight of theaircraft 108.

In an alternative implementation, the flight data parser 172 determinesthe parameter value changes based on a second sorted version of theentries 290 where the entries 290 are sorted based on differencesbetween the pressure altitude and the target altitude 315. For example,the entry 242 is the next entry subsequent to the first entry in thesecond sorted version of the entries 290 because a first sorting value(e.g., abs (pressure altitude 206−target altitude 315)) of the entry 242is next and subsequent to a second sorting value of the first entry(e.g., abs (pressure altitude of the first entry−target altitude 315)).In a particular aspect, each of the first entry and the entry 242correspond to flight data collected during a single flight of theaircraft 108. In an alternative aspect, the first entry corresponds toflight data collected during a first flight of the aircraft 108 and theentry 242 corresponds to flight data collected during a second flight ofthe aircraft 108. The first flight is prior to or subsequent to thesecond flight.

The flight data parser 172 filters out entries that correspond to largechanges (e.g., a larger than threshold altitude change, a larger thanthreshold fuel flow change, a larger than threshold AOA change) betweenthe adjacent entries of the sorted version of the entries 290. Forexample, the flight data parser 172 determines whether the entry 242satisfies the change threshold conditions based on the parameter valuechanges. To illustrate, the flight data parser 172, in response todetermining that the entry 242 is a next entry subsequent to the firstentry of the entries 290 (e.g., the sorted version of the entries 290),determines an altitude change 363, a fuel flow change 365, an AOA change367, or a combination thereof. The flight data parser 172 determines thealtitude change 363 based on a difference between the pressure altitude206 and a pressure altitude of the first entry. The flight data parser172 determines the fuel flow change 365 based on a difference betweenthe fuel flow 222 and a fuel flow of the first entry. The flight dataparser 172 determines the AOA change 367 based on a difference betweenthe AOA 228 and an AOA of the first entry.

In a particular aspect, the flight data parser 172 determines that theentry 242 fails to satisfy the filter criterion 301 in response todetermining that the altitude change 363 fails to satisfy (e.g., isgreater than) the altitude change threshold 357. In a particular aspect,the flight data parser 172 determines that the entry 242 fails tosatisfy the filter criterion 301 in response to determining that thefuel flow change 365 fails to satisfy (e.g., is greater than) the fuelflow change threshold 359. In a particular aspect, the flight dataparser 172 determines that the entry 242 fails to satisfy the filtercriterion 301 in response to determining that the AOA change 367 failsto satisfy (e.g., is greater than) the AOA change threshold 361. In aparticular aspect, the flight data parser 172 uses the change thresholdconditions (e.g., the altitude change threshold 357, the fuel flowchange threshold 359, the AOA change threshold 361, or a combinationthereof) to filter out entries that correspond to flight data thatindicates large changes (e.g., the altitude change 363, the fuel flowchange 365, the AOA change 367) between adjacent entries.

The flight data parser 172 removes any of the entries 290 that fail tosatisfy the filter criterion 301. For example, the flight data parser172, in response to determining that the entry 242 fails to satisfy thefilter criterion 301, removes the entry 242 from the entries 290.Alternatively, the flight data parser 172, in response to determiningthat the entry 242 satisfies the filter criterion 301, refrains fromremoving the entry 242 from the entries 290.

In some aspects, the flight data parser 172 determines whether theentries 290 satisfy the filter criterion 301 and refrains fromdetermining whether the entries 290 satisfy the filter criterion 303. Inan alternative aspect, the flight data parser 172 determines whether theentries 290 satisfy the filter criterion 303 in addition or as analternative to satisfying the filter criterion 301. For example, theflight data parser 172 filters out entries that correspond to relativelylarge fuel mileage deviations (e.g., outside of a two standard deviationlimit) among the entries 290 (e.g., the remaining entries that satisfythe filter criterion 301). To illustrate, the flight data parser 172determines whether the entries 290 (e.g., the remaining entries thatsatisfy the filter criterion 301) satisfy relative fuel mileageconditions. The flight data parser 172, subsequent to removing any ofthe entries 290 that fail to satisfy the filter criterion 301,determines statistics for the remaining entries that satisfy the filtercriterion 301. For example, the flight data parser 172 determines a fuelmileage mean 369 (e.g., an average fuel mileage) and a fuel mileagestandard deviation 371 based on fuel mileage of the remaining entriesthat satisfy the filter criterion 301. To illustrate, the flight dataparser 172, in response to determining that the entry 242 satisfies thefilter criterion 301, determines the fuel mileage mean 369, the fuelmileage standard deviation 371, or both, based at least in part on thefuel mileage 282.

In a particular aspect, the flight data parser 172 determines whetherthe entries 290 (e.g., the remaining entries that satisfy the filtercriterion 301) satisfy the filter criterion 303. For example, the flightdata parser 172, in response to determining that the entry 242 satisfiesthe filter criterion 301, determines whether the entry 242 satisfies thefilter criterion 303. To illustrate, the flight data parser 172determines that the entry 242 fails to satisfy the filter criterion 303in response to determining that the fuel mileage 282 fails to satisfy(e.g., is greater than) a fuel mileage threshold 375 (e.g., |the fuelmileage 282|>the fuel mileage threshold 375). The fuel mileage threshold375 is based on the fuel mileage mean 369 and the fuel mileage standarddeviation 371 (e.g., the fuel mileage threshold 375=the fuel mileagemean 369+2 (the fuel mileage standard deviation 371)). In a particularaspect, the flight data parser 172 determines that the entry 242 failsto satisfy the filter criterion 303 in response to determining that thefuel mileage 282 fails to satisfy a two standard deviation limit. In aparticular aspect, the flight data parser 172 uses the filter criterion303 (e.g., the fuel mileage threshold 375) to filter out entries thatcorrespond to flight data that indicates relatively large fuel mileage(e.g., |the fuel mileage 282|>the fuel mileage threshold 375).

In a particular aspect, the flight data parser 172 removes any of theentries 290 that fail to satisfy the filter criterion 303. For example,the flight data parser 172, in response to determining that the entry242 fails to satisfy the filter criterion 303, removes the entry 242from the entries 290. Alternatively, the flight data parser 172, inresponse to determining that the entry 242 satisfies the filtercriterion 303, refrains from removing the entry 242 from the entries290.

The historical flight data 107 thus includes entries that satisfy afilter criterion (e.g., the filter criterion 301, the filter criterion303, or both). For example, the flight data parser 172 removes anyoutliers from the entries 290 that fail to satisfy the overall thresholdconditions, the change threshold conditions, the relative fuel mileageconditions, or a combination thereof. The entries 290 (e.g., theremaining entries that satisfy the filter criterion 301, the filtercriterion 303, or both) correspond to data points that arerepresentative of cruise performance of the aircraft 108 of FIG. 1.

Referring to FIG. 4, a diagram 400 illustrates aspects of thetail-specific parameter generator 174 and the memory 122. Thetail-specific parameter generator 174 has access to the nominal aircraftperformance model 185 corresponding to the aircraft type 187 of theaircraft 108 of FIG. 1.

The tail-specific parameter generator 174 generates (or updates) thetail-specific aircraft performance model 181 based on the nominalaircraft performance model 185 and the historical flight data 107. In aparticular example, the tail-specific parameter generator 174 determinesa fuel flow bias 402 based on the historical flight data 107. Toillustrate, the nominal aircraft performance model 185 indicates anestimated fuel flow 451 corresponding to the gross weight 264, the Machnumber 202, the pressure altitude 206, the ISA deviation 232, or acombination thereof. The tail-specific parameter generator 174determines the fuel flow bias 402 based on a comparison of the estimatedfuel flow 451 and the fuel flow 222. In a particular aspect, thetail-specific parameter generator 174 determines a first set of valuescorresponding to the fuel flow 222 (e.g., detected fuel flow) for eachof the entries 290 (e.g., entries that satisfy the filter criterion 301and the filter criterion 303 of FIG. 3). The tail-specific parametergenerator 174 determines a second set of values corresponding to theestimated fuel flow 451 for each of the entries 290.

The tail-specific parameter generator 174 determines the fuel flow bias402 such that applying the fuel flow bias 402 to the second set ofvalues improves a fit (e.g., reduces an error) between the second set ofvalues and the first set of values. In a particular aspect, thetail-specific parameter generator 174 determines the fuel flow bias 402to reduce a sum of squared error for the third set of values and thefirst set of values. For example, a squared error for the entry 242 isbased on the following Equation:

e _(i) ²=(FF _(i)−β_(fuel) FF _(INFLTi))²  Equation 9

where i corresponds to the entry 242, e_(i) ² corresponds to the squarederror for the entry 242, FF_(i) corresponds to the fuel flow 222,β_(fuel) corresponds to the fuel flow bias 402, and FF_(INFLTi)corresponds to the estimated fuel flow 451.

A sum of the squared error (E) of the entries 290 is based on thefollowing Equation:

E=Σ _(i=1) ^(N) e _(i) ²=Σ_(i=1) ^(N)(FF _(i)−β_(fuel) FF_(INFLTi))²  Equation 10

where i corresponds to an ith entry (e.g., the entry 242), e_(i) ²corresponds to the squared error for the ith entry (e.g., the entry242), FF_(i) corresponds to the fuel flow 222 for the ith entry (e.g.,the entry 242), β_(fuel) corresponds to the fuel flow bias 402, andFF_(INFLTi) corresponds to the estimated fuel flow 451 for the ith entry(e.g., the entry 242).

A minimum value (e.g., 0) for the sum of the squared error (E) of theentries 290 is based on the following Equation:

$\begin{matrix}{\frac{dE}{d\;\beta_{fuel}} = {{2{\sum_{i = 1}^{N}{\left( {{FF_{i}} - {\beta_{fuel}FF_{INFLTi}}} \right)\left( {{- F}F_{INFLTi}} \right)}}} = 0}} & {{Equation}\mspace{14mu} 11}\end{matrix}$

In this aspect, the tail-specific parameter generator 174 determines thefuel flow bias 402 based on the following Equation:

$\begin{matrix}{\beta_{fuel} = \frac{\Sigma_{i = 1}^{N}FF_{INFLTi}FF_{i}}{\Sigma_{i = 1}^{N}FF_{INFLTi}^{2}}} & {{Equation}\mspace{14mu} 12}\end{matrix}$

It should be understood that determining the fuel flow bias 402corresponding to a reduced (e.g., minimized) sum of squared error isprovided as an illustrative example. In other implementations, thetail-specific parameter generator 174 uses other techniques of reducinga difference between the first set of values (e.g., detected fuel flow)and the third set of values (e.g., estimated fuel flow adjusted by thefuel flow bias 402). In a particular implementation, the tail-specificparameter generator 174 determines the fuel flow bias 402 that reducesan average difference between the first set of values (e.g., detectedfuel flow) and the third set of values (e.g., estimated fuel flowadjusted by the fuel flow bias 402).

The tail-specific parameter generator 174 generates (or updates) thetail-specific aircraft performance model 181 by updating the nominalaircraft performance model 185 based on the fuel flow bias 402. Forexample, the nominal aircraft performance model 185 is configured tooutput the estimated fuel flow 451 corresponding to the gross weight264, the Mach number 202, the pressure altitude 206, the ISA deviation232, or a combination thereof. The tail-specific aircraft performancemodel 181 is configured to determine an estimated fuel flow 461(associated with the entry 242) corresponding to the gross weight 264,the Mach number 202, the pressure altitude 206, the ISA deviation 232,or a combination thereof. The estimated fuel flow 461 is based on theestimated fuel flow 451 and the fuel flow bias 402 (e.g., estimated fuelflow 461=(fuel flow bias 402) (estimated fuel flow 451)).

In a particular aspect, the tail-specific aircraft performance model 181indicates that an estimated gross weight corresponds to a plurality ofinput parameters. The plurality of input parameters includes (or isbased on) values indicated in the historical flight data 107. Forexample, an estimated gross weight 453 (associated with the entry 242)is based on the plurality of input parameters including the fuel weight224, the estimated fuel flow 451, the gross weight 264, the pressureratio 236, the static air temperature 230, the ISA deviation 232, theAOA 228, the stabilizer trim setting 226, the Mach number 202, or acombination thereof. To illustrate, the plurality of input parametersincludes a first input parameter (e.g., the fuel weight 224/a referencefuel weight), a second input parameter (e.g., the estimated fuel flow451), a third input parameter (e.g., (the estimated fuel flow 451)²), afourth input parameter (e.g., a high gross weight indicator), a fifthinput parameter (e.g., the pressure ratio 236), a sixth input parameter(e.g., a sum of the static air temperature 230 and the ISA deviation232), a seventh input parameter (e.g., the AOA 228), an eighth inputparameter (e.g., (the AOA 228)²), a ninth input parameter (e.g., (theAOA 228)³), a tenth input parameter (e.g., the stabilizer trim setting226), an eleventh input parameter (e.g., (the stabilizer trim setting226)²), a twelfth input parameter (e.g., the Mach number 202), athirteenth input parameter (e.g., (the Mach number 202)²), one or moreadditional input parameters, or a combination thereof. In a particularaspect, the tail-specific parameter generator 174 determines that thehigh gross weight indicator has a first value (e.g., 1) in response todetermining that the gross weight 264 fails to satisfy (e.g., is greaterthan) a gross weight threshold. Alternatively, the tail-specificparameter generator 174 determines that the high gross weight indicatorhas a second value (e.g., 0) in response to determining that the grossweight 264 satisfies (e.g., is less than or equal to) the gross weightthreshold. In a particular aspect, the gross weight threshold, thereference fuel weight (e.g., 40000 lb), or both, correspond to a userinput, a configuration setting, default data, or a combination thereof.

In a particular aspect, the estimated gross weight 453 corresponds to aweighted sum of the plurality of input parameters. For example, theestimated gross weight 453 is based on the following Equation:

GW _(esti)=β₀+β₁ x _(1i)+β₂ x _(2i)+ . . . +β_(m) x _(mi)={right arrowover (x _(i)′)}{right arrow over (β)}  Equation 13

where GW_(esti) corresponds to the estimated gross weight 453, β₀corresponds to a constant intercept term, x_(i) corresponds to an ithinput parameter of the plurality of input parameters (e.g., x₁, x₂, . .. , x_(m)), β_(i) corresponds to a constant weight for the ith inputparameter, and {right arrow over (β)} (e.g., β₀, β₁, β₂, . . . β_(m))corresponds to gross weight input adjustment factors 404. Thetail-specific parameter generator 174 is configured to determine thegross weight input adjustment factors 404, as described herein.

A difference between the gross weight 264 and the estimated gross weight453 corresponds to residuals. For example, the residuals are based onthe following Equation:

r _(i) =GW _(i) −GW _(esti) =GW _(i)−{right arrow over (x _(i)′)}{rightarrow over (β)}  Equation 14

In a particular aspect, the tail-specific parameter generator 174 usesiterative linear regression (e.g., a Huber approach) to determine thegross weight input adjustment factors 404. For example, thetail-specific parameter generator 174 determines the gross weight inputadjustment factors 404 based on a weighted sum of the square of theresiduals. In a particular aspect, the weighted sum of the square of theresiduals is based on the following Equation:

Σ_(i=1) ^(N) z _(i)(GW _(i)−{right arrow over (x _(i)′)}{right arrowover (β)})²  Equation 15

where z_(i) corresponds to a weighting factor.

In a particular aspect, the tail-specific parameter generator 174determines the gross weight input adjustment factors 404 based onreducing (e.g., minimizing) the weighted sum of the square of theresiduals. For example, minimizing the weighted sum of the square of theresiduals corresponds to setting the derivative of the weighted sum ofthe square of the residuals to zero. In a particular aspect, thetail-specific parameter generator 174 is configured to reduce (e.g.,minimize) the weighted sum of the square of the residuals based on thefollowing Equation:

Σ_(i=1) ^(N) z _(i)(GW _(i)−{right arrow over (x _(i)′)}{right arrowover (β)}){right arrow over (x _(i)′)}{right arrow over (β)}  Equation15

The tail-specific parameter generator 174 determines the weightingfactor based on the residual values and a tuning parameter. For example,the tail-specific parameter generator 174 determines the weightingfactor based on the following Equation:

$\begin{matrix}{z_{i} = \left\{ \begin{matrix}{1,\ {{r_{i}} \leq k}} \\{\frac{k}{r_{i}},{{r_{i}} > k}}\end{matrix} \right.} & {{Equation}\mspace{20mu} 17}\end{matrix}$

where k corresponds to a tuning parameter. The tuning parameter is basedon the following Equation:

k=1.5σ  Equation 18

where σ corresponds to a standard deviation of estimation errors. Thestandard deviation of estimation errors is based on the followingEquation:

$\begin{matrix}{\sigma \approx \frac{MAR}{{0.6}745}} & {{Equation}\mspace{20mu} 19}\end{matrix}$

where MAR corresponds to a median absolute residual.

The weighting factor (z_(i)) depends on the residuals(r_(i)). Theresiduals(r_(i)) depend on the gross weight input adjustment factors404({right arrow over (β)}). The gross weight input adjustment factors404({right arrow over (β)}) depends on the weighting factor (z_(i)). Thetail-specific parameter generator 174 uses an iterative approach todetermining the gross weight input adjustment factors 404({right arrowover (β)}). In a particular aspect, the tail-specific parametergenerator 174 determines the gross weight input adjustment factors404({right arrow over (β)})based on the following Equation:

{right arrow over (β)}=[{right arrow over (X)} ^(T) {right arrow over(Z)}{right arrow over (X)}]⁻¹ {right arrow over (X)} ^(T) {right arrowover (Z)}({right arrow over (GW)})  Equation 20

where {right arrow over (GW)} is based on ({right arrow over(GW)})^(T)=[GW₁ GW₂ . . . GW_(N)] and GW_(i) corresponds to the grossweight 264 for an ith entry (e.g., the entry 242).

{right arrow over (X)} is based on the following Equation:

$\begin{matrix}{\overset{\rightarrow}{X} = \begin{bmatrix}x_{11} & x_{21} & \ldots & x_{m\; 1} \\x_{12} & x_{22} & \; & x_{m\; 2} \\\vdots & \; & \ddots & \vdots \\x_{1N} & x_{2N} & \ldots & x_{mN}\end{bmatrix}} & {{Equation}\mspace{14mu} 21}\end{matrix}$

where x_(ij) corresponds to a jth input parameter of the plurality ofinput parameters for the estimated gross weight 453 corresponding to theith entry (e.g., the entry 242).

{right arrow over (Z)} corresponds to diag {z_(i)}. For example, 2corresponds to a matrix with values of z_(i) in the main diagonal of thematrix.

The tail-specific parameter generator 174 determines a normalized grossweight vector ({right arrow over (GW)}), where an ith element of thenormalized gross weight vector (GW) is based on the following Equation:

$\begin{matrix}{\overset{\_}{GW_{i}} = \frac{{GW_{i}} - \mu_{GW}}{\sigma_{GW}}} & {{Equation}\mspace{14mu} 22}\end{matrix}$

where μ_(GW) corresponds to a mean of the estimated gross weight 453corresponding to each of the entries 290 and σ_(GW) corresponds to astandard deviation of the estimated gross weight 453 corresponding toeach of the entries 290. The normalized gross weight vector (GW) has adimension of N.

The tail-specific parameter generator 174 determines a normalized inputparameter matrix (X), where ji^(th) element of the normalized inputparameter matrix (X) is based on the following Equation:

$\begin{matrix}{{\overset{\_}{x}}_{ji} = \frac{x_{ji} - \mu_{x_{j}}}{\sigma_{x_{j}}}} & {{Equation}\mspace{14mu} 23}\end{matrix}$

where μ_(x) _(j) corresponds to a mean of the jth column of {right arrowover (X)} and σ_(x) _(j) corresponds to a standard deviation of the jthcolumn of {right arrow over (X)}. The normalized input parameter matrix(X) has a dimension of N×m.

The normalized gross weight vector (GW) is related to the normalizedinput parameter matrix (X) based on the following Equation:

GW=X β   Equation 24

where β corresponds to an m element vector of normalized weights. In aparticular aspect, β does not include an intercept term because allvalues have been normalized to zero mean. In a particular aspect, thevector of normalized weights (β) is related to (but differs from) thevector of gross weight input adjustment factors 404 (β), as describedherein.

The tail-specific parameter generator 174 uses an iterative leastsquares approach to determine the vector of normalized weights (β). Forexample, the tail-specific parameter generator 174 determines an initialestimate for the normalized weights vector β ⁽⁰⁾ using a least squaresestimate with all weighting factors in objective function equal to one.To illustrate, the tail-specific parameter generator 174 determines theinitial estimate for the normalized weights vector β ⁽⁰⁾ based on thefollowing Equation:

β ⁽⁰⁾=[ X ^(T) X ]⁻¹ X ^(T)( GW )  Equation 25

The tail-specific parameter generator 174 determines an initialresiduals vector based on the initial estimate for the normalizedweights vector β ⁽⁰⁾. For example, the tail-specific parameter generator174 determines the initial residuals vector based on the followingEquation:

r =( GW )− Xβ ⁽⁰⁾  Equation 26

The tail-specific parameter generator 174 determines a median absoluteresidual (MAR) based on the initial residuals vector. For example, thetail-specific parameter generator 174 determines the MAR based on thefollowing Equation:

MAR=median(| r _(i)−median( r )|)  Equation 27

The tail-specific parameter generator 174, at each iteration t,determines residuals r ^((t−1)) and associated weights z_(i). Forexample, the tail-specific parameter generator 174 determines theresiduals r ^((t−1)) based on the following Equation:

r ^((t−1)) =GW _(i)− x _(i) ′β ^((t−1))  Equation 28

The tail-specific parameter generator 174 determines the associatedweights z_(i) based on the following Equation:

$\begin{matrix}{z_{i} = \left\{ {{\begin{matrix}{1,{{{\overset{\_}{r}}_{i}} \leq k}} \\{\frac{k}{{\overset{\_}{r}}_{i}},{{{\overset{\_}{r}}_{i}} > k}}\end{matrix}{where}\mspace{14mu} k} = {1.5{\left( \frac{MAR}{0.6745} \right).}}} \right.} & {{Equation}\mspace{14mu} 29}\end{matrix}$

The tail-specific parameter generator 174 determines an estimate for anormalized beta vector β^((t)) corresponding to the residuals r ^((t−1))and associated weights z_(i). For example, the tail-specific parametergenerator 174 determines the estimate for the normalized beta vector β^((t)) based on the following Equation:

{right arrow over (β)}^((t))=[{right arrow over (X)} ^(T) {right arrowover (Z)} ^((t−1)) {right arrow over (X)}]⁻¹ {right arrow over (X)} ^(T){right arrow over (Z)} ^((t−1))( GW )  Equation 30

where the weight matrix Z^((t−1)) is based on the following Equation:

Z ^((t−1))=_(diag) {z _(i) ^((t−1))}⁻¹  Equation 31

The tail-specific parameter generator 174 performs iterations until theestimated normalized beta vectors converge. The tail-specific parametergenerator 174 determines, from GW=X β, that a single normalized grossweight is related to a single normalized input parameter set accordingto the following Equation:

$\begin{matrix}{\frac{{GW_{i}} - \mu_{GW}}{\sigma_{GW}} = {\sum\limits_{j = 1}^{m}{\left( \frac{x_{ji} - \mu_{x_{j}}}{\sigma_{x_{j}}} \right){\overset{\_}{\beta}}_{j}}}} & {{Equation}\mspace{14mu} 32}\end{matrix}$

Solving for GW_(i) yields the following Equation:

$\begin{matrix}{{GW_{i}} = {{\sigma_{GW}{\sum\limits_{j = 1}^{m}{\left( \frac{x_{ji} - \mu_{x_{j}}}{\sigma_{x_{j}}} \right){\overset{\_}{\beta}}_{j}}}} + \mu_{GW}}} & {{Equation}\mspace{14mu} 33}\end{matrix}$

Expanding the summation yields the following Equation:

$\begin{matrix}{{GW_{i}} = {{{\sigma_{GW}\left( \frac{x_{1i} - \mu_{x_{1}}}{\sigma_{x_{1}}} \right)}{\overset{\_}{\beta}}_{1}} + {{\sigma_{GW}\left( \frac{x_{2i}\mu_{x_{2}}}{\sigma_{x_{2}}} \right)}{\overset{\_}{\beta}}_{2}} + \ldots + {{\sigma_{GW}\left( \frac{x_{mi}\mu_{x_{m}}}{\sigma_{x_{m}}} \right)}{\overset{\_}{\beta}}_{m}} + \mu_{GW}}} & {{Equation}\mspace{14mu} 34}\end{matrix}$

Rearranging yields the following Equation:

$\begin{matrix}{{GW}_{i} = {{\frac{\sigma_{GW}}{\sigma_{x_{1}}}{\overset{\_}{\beta}}_{1}x_{1i}} + {\frac{\sigma_{GW}}{\sigma_{x_{2}}}{\overset{\_}{\beta}}_{2}x_{2i}} + \ldots + {\frac{\sigma_{GW}}{\sigma_{x_{m}}}{\overset{\_}{\beta}}_{m}x_{mi}} + \mu_{GW} - {\frac{\sigma_{GW}}{\sigma_{x_{1}}}{\overset{\_}{\beta}}_{1}\mu_{x_{1}}} - {\frac{\sigma_{GW}}{\sigma_{x_{2}}}{\overset{\_}{\beta}}_{2}\mu_{x_{2}}} - \ldots - {\frac{\sigma_{GW}}{\sigma_{x_{m}}}{\overset{\_}{\beta}}_{m}\mu_{x_{m}}}}} & {{Equation}\mspace{14mu} 35}\end{matrix}$

Comparing Equation 35 to Equation 13 (e.g.,GW_(esti)=β₀+β₁x_(1i)+β₂x_(2i)+ . . . +β_(m)x_(mi)={right arrow over (x_(i)′)}{right arrow over (β)}), the constant weights β_(j) are relatedto the normalized weights β _(j) according to the following Equation:

$\begin{matrix}{{\beta_{j} = {\frac{\sigma_{GW}}{\sigma_{x_{j}}}{\overset{\_}{\beta}}_{j}}},{j = 1},2,\ldots\mspace{14mu},m} & {{Equation}\mspace{14mu} 36}\end{matrix}$

and the intercept corresponds to the following Equation:

$\begin{matrix}{\beta_{0} = {\mu_{GW} - {\frac{\sigma_{GW}}{\sigma_{x_{1}}}{\overset{\_}{\beta}}_{1}\mu_{x_{1}}} - {\frac{\sigma_{GW}}{\sigma_{x_{2}}}{\overset{\_}{\beta}}_{2}\mu_{x_{2}}} - \ldots - {\frac{\sigma_{GW}}{\sigma_{x_{m}}}{\overset{\_}{\beta}}_{m}\mu_{x_{m}}}}} & {{Equation}\mspace{14mu} 37}\end{matrix}$

Rewriting yields the following Equation:

β₀=μ_(GW)−β₁μ_(x) ₁ −β₂μ_(x) ₂ − . . . −β_(m)μ_(x) _(m)   Equation 38

The tail-specific parameter generator 174 thus determines the grossweight input adjustment factors 404. In a particular aspect, the grossweight input adjustment factors 404 include a first adjustment factor(e.g., β₁=1945.05850772936), a second adjustment factor (e.g.,β₂=63.64717988524), a third adjustment factor (e.g.,β₃=0.00283351843837169), a fourth adjustment factor (e.g.,β₄=608.193281131959), a fifth adjustment factor (e.g.,β₅=−176687.200575113), a sixth adjustment factor (e.g.,β₆=−103.693898395283), a seventh adjustment factor (e.g.,β₇=89247.9687202538), an eighth adjustment factor (e.g.,β₀=12116.1469156739), a ninth adjustment factor (e.g.,β₉=518.788789927141), a tenth adjustment factor (e.g.,β₁₀=−22796.8715364357), an eleventh adjustment factor (e.g.,β₁₁=1874.40317603838), a twelfth adjustment factor (e.g.,β₁₂=711667.52591982), a thirteenth adjustment factor (e.g.,β₁₃=−604891.665472913), or a combination thereof. The gross weight inputadjustment factors 404 also include an intercept term (e.g.,β₀=−407864.296721922).

The tail-specific parameter generator 174 generates (or updates) thetail-specific aircraft performance model 181 by updating the nominalaircraft performance model 185 based on the gross weight inputadjustment factors 404. For example, the tail-specific aircraftperformance model 181 indicates that an estimated gross weightcorresponds to the plurality of input parameters and the gross weightinput adjustment factors 404. To illustrate, the tail-specific aircraftperformance model 181 is configured to determine that the estimatedgross weight 453 (associated with the entry 242) is equal to a sum ofthe intercept term (e.g., β₀), a first weighted input parameter (e.g.,the first adjustment factor*the fuel weight 224), a second weightedinput parameter (e.g., the second adjustment factor*the estimated fuelflow 451), the third weighted input parameter (e.g., the thirdadjustment factor*(the estimated fuel flow 451)²), the fourth weightedinput parameter (e.g., the fourth adjustment factor*a high gross weightindicator), the fifth weighted input parameter (e.g., the fifthadjustment factor*the pressure ratio 236), the sixth weighted inputparameter (e.g., the sixth adjustment factor*(a sum of the static airtemperature 230 and the ISA deviation 232)), the seventh weighted inputparameter (e.g., the seventh adjustment factor*the AOA 228), the eighthweighted input parameter (e.g., the eighth adjustment factor*(the AOA228)²), the ninth weighted input parameter (e.g., the ninth adjustmentfactor*(the AOA 228)³), the tenth weighted input parameter (e.g., thetenth adjustment factor*the stabilizer trim setting 226), the eleventhweighted input parameter (e.g., the eleventh adjustment factor*(thestabilizer trim setting 226)²), the twelfth weighted input parameter(e.g., the twelfth adjustment factor*the Mach number 202), thethirteenth weighted input parameter (e.g., the thirteenth adjustmentfactor*(the Mach number 202)²), or a combination thereof.

In a particular aspect, the tail-specific aircraft performance model 181is configured to indicate an implicit cost index 406 associated with theaircraft 108. For example, the tail-specific parameter generator 174determines entry implicit cost indices 459 corresponding to the entries290 and determines the implicit cost index 406 based on the entryimplicit cost indices 459. To illustrate, the tail-specific parametergenerator 174 determines an entry implicit cost index 455 (correspondingto the entry 242) based on the Mach number 202, the ground speed 220,the true airspeed 238, a particular gross weight, the pressure ratio236, the temperature ratio 234, or a combination thereof. In aparticular aspect, the entry implicit cost index 455 indicates anestimated operating cost of the aircraft 108 during a flightcorresponding to the entry 242. In a particular aspect, the particulargross weight corresponds to the gross weight 264. In an alternativeaspect, the particular gross weight corresponds to the estimated grossweight 453. In a particular implementation, the tail-specific parametergenerator 174 uses a cost index calculator (e.g., an Econ cruise speedtable) that maps the Mach number 202, the ground speed 220, the trueairspeed 238, a particular gross weight, the pressure ratio 236, thetemperature ratio 234, or a combination thereof, to the entry implicitcost index 455. For example, the entry implicit cost index 455 is basedon the following Equation:

$\begin{matrix}{{CI}_{deducedi} = {{f\left( {M_{i},{{VG}S_{i}},{{VTA}S_{i}},{GW}_{i},\delta_{i},\theta_{i}} \right)}{\frac{lb}{100}/{hr}}}} & {{Equation}\mspace{14mu} 39}\end{matrix}$

where CI_(deducedi) corresponds to the entry implicit cost index 455,M_(i) corresponds to the Mach number 202, VGS_(i) corresponds to theground speed 220, VTAS_(i) corresponds to the true airspeed 238, GW_(i)corresponds to the gross weight 264 or the estimated gross weight 453,β_(i) corresponds to the pressure ratio 236, θ_(i) corresponds to thetemperature ratio 234, and f corresponds to a function (e.g., a mapping)of the cost index calculator (e.g., an Econ cruise speed table).

The tail-specific parameter generator 174 determines the implicit costindex 406 as an average (e.g., a mean) of the entry implicit costindices 459. For example, the implicit cost index 406 is based on thefollowing Equation:

$\begin{matrix}{{CI}_{deducedi} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{CI}_{deducedi}}}} & {{Equation}\mspace{14mu} 40}\end{matrix}$

where CI_(deduced) corresponds to the implicit cost index 406. In aparticular aspect, the historical flight data 107 that is collected overmultiple flights of the aircraft 108 of FIG. 1 and the implicit costindex 406 (e.g., 19.79031) is an average of the entry implicit costindices 459 corresponding to the multiple flights of the aircraft 108.

In a particular aspect, the tail-specific aircraft performance model 181is configured to indicate a flight cost 416 (e.g., a minimum operatingcost), a cost index 418, or both. For example, the tail-specificparameter generator 174 determines flight costs 469 corresponding to theentries 290. To illustrate, the tail-specific parameter generator 174determines an entry flight cost 465 corresponding to the entry 242 basedon the following Equation:

C _(nm) =CF0(100CI+FF)/VGS$/nm  Equation 41

where C_(nm) corresponds to the entry flight cost 465, CFO correspondsto a fuel price 422, CI corresponds to a cost index 424, FF correspondsto the fuel flow 222, and VGS corresponds to the ground speed 220. In aparticular aspect, the entry flight cost 465 is in terms of dollars (S)per nautical mile (nm). In a particular aspect, the fuel price 422(e.g., an assumed fuel price) corresponds to user input, a configurationsetting, default data, or a combination thereof. In a particular aspect,the cost index 424 corresponds to the target cost index 189 of FIG. 1,an average cost index associated with an operator (e.g., an airline) ofthe aircraft 108, or both. The entry flight cost 465 indicates atime-related cost and a fuel-related cost associated with the entry 242.

The tail-specific parameter generator 174 determines normalized costindices 429 corresponding to the entries 290. For example, thetail-specific parameter generator 174 determines a normalized cost index425 corresponding to the entry 242 based on the following Equation:

$\begin{matrix}{Q_{1} = \frac{{CI}_{deduced}}{\left( \frac{GW}{10^{ó}\delta} \right)}} & {{Equation}\mspace{14mu} 42}\end{matrix}$

where Q₁ corresponds to the normalized cost index 425, CI_(deduced)corresponds to the implicit cost index 406, GW corresponds to aparticular gross weight, and δ corresponds to the pressure ratio 236. Ina particular aspect, the particular gross weight includes the grossweight 264. In an alternative aspect, the particular gross weightincludes the estimated gross weight 453.

The tail-specific parameter generator 174 uses a curve-fitting techniqueto fit the flight costs 469 to the normalized cost indices 429. Forexample, the tail-specific parameter generator 174 uses an exponentialmodel to perform the curve-fitting. The tail-specific parametergenerator 174 determines model parameters based on the curve-fitting.For example, the tail-specific parameter generator 174 determines afirst model parameter (c₁), a second model parameter (c₂), a third modelparameter (λ₁), a fourth model parameter (λ₂), or a combination thereof.To illustrate, the tail-specific parameter generator 174 uses nonlinearoptimization technique (e.g., the Nelder-Mead algorithm) to determinethe exponents of a non-linear component (e.g., the third model parameter(λ₁), the fourth model parameter (λ₂), or both) and uses linearregression to determine coefficients (e.g., the first model parameter(c₁), the second model parameter (c₂), or both). In a particular aspect,the third model parameter (λ₁), the fourth model parameter (λ₂), orboth, have negative values. A cost model corresponding to the modelparameters is based on the following Equation:

=c ₁ e ^(−λ) ¹ ^(Q) ¹⁺ c ₂ e ^(−λ) ² ^(Q) ¹   Equation 43

where

corresponds to the cost model.

The tail-specific parameter generator 174 determines the flight cost 416(e.g., a minimum operating cost) based on the cost model. For example,the flight cost 416 corresponds to a lowest value of the normalized costindex 425 (Q₁) corresponding to the cost model. To illustrate, setting aderivative of the cost model to zero and solving for Q₁ yields theflight cost 416. The flight cost 416 corresponds to an estimated minimumoperating cost of the aircraft 108. In a particular aspect, theparticular gross weight factor is removed in solving for Q₁. In aparticular example, the flight cost 416 is based on the followingEquation:

$\begin{matrix}{Q_{1C_{\min}} = \frac{\ln\left( {- \frac{c_{1}\lambda_{1}}{c_{2}\lambda_{2}}} \right)}{10^{ó}\left( {\lambda_{1} - \lambda_{2}} \right)}} & {{Equation}\mspace{14mu} 44}\end{matrix}$

The tail-specific parameter generator 174 determines the cost index 418for a particular entry based on the flight cost 416. For example, thetail-specific parameter generator 174 determines the cost index 418 thatcorresponds to the entry 242 at the flight cost 416 (e.g., a minimumflight cost). To illustrate, the cost index 418 indicates a predictedcost index that would have resulted if the entry 242 were related to aflight that operated at the flight cost 416 (e.g., the estimated minimumoperating cost). The tail-specific parameter generator 174 determinesthe cost index 418 for a particular entry (e.g., a particular cruiseflight condition) based on the following Equation:

$\begin{matrix}{{CI}_{C_{\min}} = {Q_{1C_{\min}}*\left( \frac{GW}{\delta} \right)}} & {{Equation}\mspace{14mu} 45}\end{matrix}$

where CI_(C) _(min) corresponds to the cost index 418 for a particularentry, Q_(1C) _(min) corresponds to the flight cost 416, GW correspondsto a particular gross weight for the particular entry, δ corresponds tothe pressure ratio 236 for the particular entry. In a particular aspect,the particular gross weight corresponds to the gross weight 264. In analternative aspect, the particular gross weight corresponds to theestimated gross weight 453.

Referring to FIG. 5, a diagram 500 illustrates aspects of therecommendation generator 176 and the memory 122. The recommendationgenerator 176 has access to the tail-specific parameters 141, thetail-specific aircraft performance model 181, or a combination thereof.For example, the recommendation generator 176 receives the tail-specificparameters 141, the tail-specific aircraft performance model 181, orboth, from the tail-specific parameter generator 174.

The recommendation generator 176 has access (e.g., in real-time) to theflight data 105 generated by the sensors 142 during a flight. The flightdata 105 indicates a plurality of parameters. For example, the flightdata 105 indicates speed information (e.g., a Mach number 502, a groundspeed 520, or both), location information (e.g., a pressure altitude506, a latitude 516, or both), attitude information (e.g., a left AOA510, a right AOA 512, pitch (e.g., left pitch 514), a heading 518, or acombination thereof), ambient environment conditions (e.g., a total airtemperature 508), weight information (e.g., a gross weight 564), fuelinformation (e.g., a fuel flow 522, a fuel weight 524, or both),settings (e.g., a stabilizer trim setting 526), or a combinationthereof. In a particular aspect, the recommendation generator 176determines (by performing similar calculations as the flight data parser172 as described with reference to of FIG. 2) a gross weight over delta540, a static air temperature 530, an ISA deviation 532, a temperatureratio 534, a pressure ratio 536, a true airspeed 538, an AOA 528, a fuelmileage 582, a data collection time 590, or a combination thereof, basedon the flight data 105.

In a particular aspect, the gross weight 564 corresponds to a grossweight indicated in the flight data 105 as received from the sensors142. In an alternative aspect, the flight data 105 received from theinstruments indicates a first gross weight and the recommendationgenerator 176 determines the gross weight 564 based on the first grossweight and a gravitational variation adjustment 567 (e.g., the grossweight 564=the first gross weight+the gravitational variation adjustment567). In a particular aspect, the recommendation generator 176determines the gravitational variation adjustment 567 by performingsimilar calculations as performed by the flight data parser 172 todetermine the gravitational variation adjustment 267, as described withreference to FIG. 2. In an alternative aspect, the tail-specificaircraft performance model 181 indicates the gravitational variationadjustment 267 and the recommendation generator 176 determines thegravitational variation adjustment 567 based on the gravitationalvariation adjustment 267. In a particular example, the gravitationalvariation adjustment 567 is the same as the gravitational variationadjustment 267.

In a particular aspect, the recommendation generator 176 determineswhether the flight data 105 satisfies the filter criterion 301, thefilter criterion 303, or both. For example, the recommendation generator176 performs similar calculations as performed by the flight data parser172, as described with reference to FIG. 3. The recommendation generator176, in response to determining that the flight data 105 satisfies thefilter criterion 301, the filter criterion 303, or both, generates therecommendation 191 based on the flight data 105. Alternatively, therecommendation generator 176, in response to determining that the flightdata 105 fails to satisfy the filter criterion 301, the filter criterion303, or both, refrains from generating the recommendation 191 based onthe flight data 105.

The recommendation generator 176 is configured to determine therecommended cost index 193 based on the tail-specific aircraftperformance model 181. For example, the recommendation generator 176, inresponse to determining that the tail-specific aircraft performancemodel 181 indicates the flight cost 416, determines the recommended costindex 193 based on the flight cost 416. To illustrate, therecommendation generator 176 determines recommended cost index 193 basedon Equation 45, where CI_(C) _(min) corresponds to the recommended costindex 193, Q_(1C) _(min) corresponds to the flight cost 416, GWcorresponds to the gross weight 564, and δ corresponds to the pressureratio 536.

In a particular aspect, the recommendation generator 176 is configuredto use altitude calculation techniques (e.g., an optimal performancealtitude calculation technique) to determine the recommended cruisealtitude 195. For example, the recommendation generator 176 determinesthe recommended cruise altitude 195 based on an estimated gross weight544, the total air temperature 508, a cost index 542, a center ofgravity, an allowance for buffet boundary limitations, estimated windspeeds, or a combination thereof. To illustrate, the recommendationgenerator 176 determines the cost index 542 based on Equation 45, whereCI_(C) _(min) corresponds to the cost index 542, Q_(1C) _(min)corresponds to the flight cost 416, GW corresponds to the estimatedgross weight 555, and δ corresponds to the pressure ratio 536.

The recommendation generator 176 determines the estimated gross weight555 based on the gross weight input adjustment factors 404 and theplurality of parameters. To illustrate, the recommendation generator 176determines the estimated gross weight 555 based on the followingEquation:

GW _(est)=β₀+β₁ x ₁+β₂ x ₂+ . . . +β_(m) x _(m)  Equation 46

where GW_(est) corresponds to the estimated gross weight 555. β₀corresponds to the intercept term indicated by the gross weight inputadjustment factors, as described with reference to FIG. 4. β₁, β₂, . . ., β_(m) correspond to the gross weight input adjustment factors 404, asdescribed with reference to FIG. 4. x₁, x₂, . . . , x_(m) correspond toparameters indicated by the flight data 105.

In a particular aspect, the estimated gross weight 555 corresponds to asum of the intercept term, a first weighted input parameter (e.g., thefirst adjustment factor*the fuel weight 524), a second weighted inputparameter (e.g., the second adjustment factor*an estimated fuel flow551), a third weighted input parameter (e.g., the third adjustmentfactor*(the estimated fuel flow 551)²), a fourth weighted inputparameter (e.g., the fourth adjustment factor*a high gross weightindicator 553), a fifth weighted input parameter (e.g., the fifthadjustment factor*the pressure ratio 536), a sixth weighted inputparameter (e.g., the sixth adjustment factor*(a sum of the static airtemperature 530 and the ISA deviation 532)), the seventh weighted inputparameter (e.g., the seventh adjustment factor*the AOA 528), the eighthweighted input parameter (e.g., the eighth adjustment factor*(the AOA528)²), the ninth weighted input parameter (e.g., the ninth adjustmentfactor*(the AOA 528)³), the tenth weighted input parameter (e.g., thetenth adjustment factor*the stabilizer trim setting 526), the eleventhweighted input parameter (e.g., the eleventh adjustment factor*(thestabilizer trim setting 526)²), the twelfth weighted input parameter(e.g., the twelfth adjustment factor*the Mach number 502), thethirteenth weighted input parameter (e.g., the thirteenth adjustmentfactor*(the Mach number 502)²), or a combination thereof.

In a particular example, the tail-specific aircraft performance model181 indicates the estimated fuel flow 551 corresponding to the fuel flowbias 402, the gross weight 564, the Mach number 502, the pressurealtitude 506, the ISA deviation 532, or a combination thereof, asdescribed with reference to FIG. 4. For example, the estimated fuel flow551 corresponds to an adjustment of a first estimated fuel flow based onthe fuel flow bias 402 (e.g., the estimated fuel flow 551=(the fuel flowbias 402) (the first estimated fuel flow)), where the first estimatedfuel flow is indicated by the nominal aircraft performance model 185 ascorresponding to the gross weight 564, the Mach number 502, the pressurealtitude 506, the ISA deviation 532, or a combination thereof. In aparticular aspect the fuel mileage 582 is based on the fuel flow 522(e.g., the fuel mileage 582=the true airspeed 538/the fuel flow 522). Ina particular aspect, the fuel mileage 582 is based on the estimated fuelflow 551 (e.g., the fuel mileage 582=the true airspeed 538/the estimatedfuel flow 551).

The recommendation generator 176 sets the high gross weight indicator553 to have a first value (e.g., 1) in response to determining that thegross weight 564 fails to satisfy (e.g., is greater than) a gross weightthreshold. Alternatively, the recommendation generator 176 sets the highgross weight indicator 553 to have a second value (e.g., 0) in responseto determining that the gross weight 564 satisfies (e.g., is less thanor equal to) the gross weight threshold. In a particular aspect, thegross weight threshold corresponds to a user input, a configurationsetting, default data, or a combination thereof.

In a particular aspect, the recommendation generator 176 determines,based on Equation 45, estimated cost indices 558 associated with aplurality of cruise altitudes 554. For example, the recommendationgenerator 176 determines an estimated gross weight 544 and an estimatedpressure ratio 557 associated with a cruise altitude 574. Therecommendation generator 176 uses Equation 45 to determine an estimatedcost index 568 associated with the cruise altitude 574, where CI_(C)_(min) corresponds to the estimated cost index 568, Q_(1C) _(min)corresponds to the flight cost 416, GW corresponds to the estimatedgross weight 544, and δ corresponds to the estimated pressure ratio 557.

In a particular aspect, the plurality of cruise altitudes 554 correspondto user input, a configuration setting, default data, or a combinationthereof. The recommendation generator 176 determines the estimated grossweight 544 based on the gross weight input adjustment factors 404 and aplurality of parameters. To illustrate, the recommendation generator 176determines the estimated gross weight 544 based on Equation 46, whereGW_(est) corresponds to the estimated gross weight 544. β₀ correspondsto the intercept term indicated by the gross weight input adjustmentfactors, as described with reference to FIG. 4. β₁, β₂, . . . , β_(m)correspond to the gross weight input adjustment factors 404, asdescribed with reference to FIG. 4. x₁, x₂, . . . , x_(m) correspond toparameters indicated by the flight data 105, associated with the cruisealtitude 574, or both. For example, first parameters that areindependent of altitude are indicated by the flight data 105 and secondparameters that are dependent on altitude are estimated based on thecruise altitude 574. To illustrate the first parameters include theestimated pressure ratio 557, an estimated ISA deviation 559, or both.

In a particular aspect, the estimated gross weight 544 corresponds to asum of the intercept term, a first weighted input parameter (e.g., thefirst adjustment factor*the fuel weight 524), a second weighted inputparameter (e.g., the second adjustment factor*the estimated fuel flow551), a third weighted input parameter (e.g., the third adjustmentfactor*(the estimated fuel flow 551)²), a fourth weighted inputparameter (e.g., the fourth adjustment factor*the high gross weightindicator 553), a fifth weighted input parameter (e.g., the fifthadjustment factor*the estimated pressure ratio 557), a sixth weightedinput parameter (e.g., the sixth adjustment factor*(a sum of the staticair temperature 530 and the estimated ISA deviation 559)), the seventhweighted input parameter (e.g., the seventh adjustment factor*the AOA528), the eighth weighted input parameter (e.g., the eighth adjustmentfactor*(the AOA 528)²), the ninth weighted input parameter (e.g., theninth adjustment factor*(the AOA 528)³), the tenth weighted inputparameter (e.g., the tenth adjustment factor*the stabilizer trim setting526), the eleventh weighted input parameter (e.g., the eleventhadjustment factor*(the stabilizer trim setting 526)²), the twelfthweighted input parameter (e.g., the twelfth adjustment factor*the Machnumber 502), the thirteenth weighted input parameter (e.g., thethirteenth adjustment factor*(the Mach number 502)²), or a combinationthereof.

The recommendation generator 176 determines the estimated pressure ratio557 based on Equation 5, where δ corresponds to the estimated pressureratio 557, h_(p) corresponds to the cruise altitude 574, and ecorresponds to Euler's number (2.718281828 . . . ). The recommendationgenerator 176 determines the estimated ISA deviation 559 based onEquation 3, where ΔISA corresponds to the estimated ISA deviation 559,SAT corresponds to the static air temperature 530, and h_(p) correspondsto the cruise altitude 574.

The recommendation generator 176 selects the recommended cruise altitude195 from the cruise altitudes 554 based on the estimated cost indices558. In a particular aspect, the recommendation generator 176, inresponse to identifying the estimated cost index 568 as the lowest amongthe estimated cost indices 558, determines that the cruise altitude 574(associated with the estimated cost index 568) corresponds to apredicted minimum operating cost cruise altitude of the aircraft 108.The recommendation generator 176 selects the cruise altitude 574 as therecommended cruise altitude 195 in response to determining that thecruise altitude 574 corresponds to the predicted minimum operating costcruise altitude of the aircraft 108.

In a particular aspect, the recommendation generator 176 determines therecommended speed 197 based on the gross weight 564, the pressure ratio536, the recommended cost index 193, or a combination thereof. Forexample, the recommendation generator 176 has access to a speedcalculator (e.g., an Econ cruise speed table) that maps the recommendedcost index 193, the ground speed 520, the true airspeed 538, the grossweight 564, the pressure ratio 536, the temperature ratio 534, or acombination thereof, to the recommended speed 197.

Referring to FIG. 6, an example of aspects of the GUI 163 is shown. In aparticular aspect, the GUI 163 is generated by the recommendationgenerator 176, the on-board computing device 102, the aircraft 108, thesystem 100 of FIG. 1, or a combination thereof.

In FIG. 6, the GUI 163 indicates the plan speed 157 (e.g., 0.780 Mach),the Mach number 502 (e.g., 0.783 Mach), and the recommended speed 197(e.g., 0.783 Mach). In a particular aspect, the Mach number 502represents a current Mach number. For example, the Mach number 502represents a recently detected Mach number for the aircraft 108. The GUI163 indicates the plan altitude 155 (e.g., 400), the pressure altitude506 (e.g., 400), and the recommended cruise altitude 195 (e.g., 409). Ina particular aspect, the plan altitude 155 represents a plan flightlevel, the pressure altitude 506 represents a current flight level, andthe recommended cruise altitude 195 represents a recommended flightlevel. The GUI 163 indicates the plan fuel mileage 159 (e.g., 9.09nm/100 lbs) and the fuel mileage 582 (e.g., 11.28 nm/100 lbs). In aparticular aspect, the fuel mileage 582 represents a current fuelmileage.

The GUI 163 indicates the target cost index 189, the recommended costindex 193, and a detected cost index 601. In a particular aspect, thetarget cost index 189 represents a current target cost index. Forexample, the recommendation generator 176 determines the detected costindex 601 based on the following Equation:

$\begin{matrix}{{CI}_{detected} = {{f\left( {M,{VGS},{VTAS},{GW},\delta,\theta} \right)}{\frac{lb}{100}/{hr}}}} & {{Equation}\mspace{14mu} 47}\end{matrix}$

where CI_(detected) corresponds to the detected cost index 601, Mcorresponds to the Mach number 502 of FIG. 5, VGS corresponds to theground speed 520, VTAS corresponds to the true airspeed 538, GWcorresponds to the gross weight 564 or the estimated gross weight 555, δcorresponds to the pressure ratio 536, 9 corresponds to the temperatureratio 534, and f corresponds to a function (e.g., a mapping) of the costindex calculator (e.g., an Econ cruise speed table).

The GUI 163 thus enables display, in real-time, of plan values, actualvalues, and recommended values for speed, altitude, fuel mileage, costindex, or a combination thereof. In some examples, the GUI 163 enables auser (e.g., a pilot) to make informed decisions during flight regardingoperation of the aircraft 108 that take into account changing flightconditions and tail-specific performance. For example, the user, basedon comparing the displayed plan values, actual values, and recommendedvalues, provides user input indicating the selected cost index 153, theselected speed 165, the selected altitude 167 of FIG. 1, or acombination thereof. In some examples, the on-board computing device 102automatically (e.g., independently of user input) sets the selected costindex 153 to the recommended cost index 193, the selected speed 165 tothe recommended speed 197, the selected altitude 167 to the recommendedcruise altitude 195, or a combination thereof. The on-board computingdevice 102 (or another component of the aircraft 108) generates, basedon the user input, one or more control commands to update an altitude ofthe aircraft 108 to the selected altitude 167, a speed of the aircraft108 to the selected speed 165, or a combination thereof, as describedwith reference to FIG. 1. Operation of the aircraft 108 based on therecommended cost index 193, the recommended speed 197, the recommendedcruise altitude 195, or a combination thereof, reduces operating costsof the aircraft 108 relative to the plan values (e.g., the target costindex 189, the plan speed 157, the plan altitude 155, or a combinationthereof).

FIG. 7 is a flowchart of a method 700 for tail-specific parametercomputation. In a particular aspect, the method 700 is performed by theflight data parser 172, the tail-specific parameter generator 174, therecommendation generator 176, the processor 170, the on-board computingdevice 102, the aircraft 108, the system 100 of FIG. 1, or anycombination thereof.

The method 700 includes receiving flight data from a databus of a firstaircraft, at 702. For example, the recommendation generator 176 of FIG.1 receives the flight data 105 from the databus 140 of the aircraft 108,as described with reference to FIG. 1.

The method 700 also includes generating, based on the flight data and atail-specific aircraft performance model, a recommended cost index and arecommended cruise altitude, at 704. For example, the recommendationgenerator 176 of FIG. 1 generates, based on the flight data 105 and thetail-specific aircraft performance model 181, the recommended cost index193 and the recommended cruise altitude 195, as described with referenceto FIGS. 1 and 5. The tail-specific aircraft performance model 181 isbased on the historical flight data 107 of the aircraft 108 and thenominal aircraft performance model 185. The nominal aircraft performancemodel 185 is associated with a second aircraft (e.g., a representativeaircraft) of the same aircraft type (e.g., the aircraft type 187) as theaircraft 108, as described with reference to FIG. 1.

The method 700 further includes providing the recommended cost index andthe recommended cruise altitude to a display device of the firstaircraft, at 706. For example, the recommendation generator 176 of FIG.1 provides the recommended cost index 193 and the recommended cruisealtitude 195 to the display device 144 of the aircraft 108. Toillustrate, the recommendation generator 176 generates the GUI 163indicating the recommended cost index 193, the recommended cruisealtitude 195, or both. The recommendation generator 176 provides the GUI163 to the display device 144.

The method 700 thus enables use of the tail-specific parameters 141(e.g., the tail-specific aircraft performance model 181 that is based onthe tail-specific parameters 141) to determine the recommended costindex 193. In some examples, the recommended cost index 193, therecommended cruise altitude 195, or both reduce (e.g., minimize)operational costs of the aircraft 108 during flight.

Aspects of the disclosure may be described in the context of theaircraft 108 as shown in FIG. 8. The aircraft 108 includes an airframe818 with a plurality of systems 820 (e.g., high-level systems) and aninterior 822. Examples of the systems 820 include one or more of apropulsion system 824, an electrical system 826, an environmental system828, a hydraulic system 830, the flight data parser 172, thetail-specific parameter generator 174, and the recommendation generator176. Other systems can also be included.

The flight data parser 172, the tail-specific parameter generator 174,the recommendation generator 176, or a combination thereof, areconfigured to support aspects of computer-implemented methods andcomputer-executable program instructions (or code) according to thepresent disclosure. For example, the flight data parser 172, thetail-specific parameter generator 174, the recommendation generator 176,or portions thereof, are configured to execute instructions to initiate,perform, or control one or more operations described with reference toFIGS. 1-7.

Although one or more of FIGS. 1-8 illustrate systems, apparatuses,and/or methods according to the teachings of the disclosure, thedisclosure is not limited to these illustrated systems, apparatuses,and/or methods. One or more functions or components of any of FIGS. 1-8as illustrated or described herein may be combined with one or moreother portions of another of FIGS. 1-8. For example, one or moreelements of the method 700 of FIG. 7 may be performed in combinationwith other operations described herein. Accordingly, no singleimplementation described herein should be construed as limiting andimplementations of the disclosure may be suitably combined withoutdeparting form the teachings of the disclosure. As an example, one ormore operations described with reference to FIGS. 1-7 may be optional,may be performed at least partially concurrently, and/or may beperformed in a different order than shown or described.

Examples described above are illustrative and do not limit thedisclosure. It is to be understood that numerous modifications andvariations are possible in accordance with the principles of the presentdisclosure.

The illustrations of the examples described herein are intended toprovide a general understanding of the structure of the variousimplementations. The illustrations are not intended to serve as acomplete description of all of the elements and features of apparatusand systems that utilize the structures or methods described herein.Many other implementations may be apparent to those of skill in the artupon reviewing the disclosure. Other implementations may be utilized andderived from the disclosure, such that structural and logicalsubstitutions and changes may be made without departing from the scopeof the disclosure. For example, method operations may be performed in adifferent order than shown in the figures or one or more methodoperations may be omitted. Accordingly, the disclosure and the figuresare to be regarded as illustrative rather than restrictive.

Moreover, although specific examples have been illustrated and describedherein, it should be appreciated that any subsequent arrangementdesigned to achieve the same or similar results may be substituted forthe specific implementations shown. This disclosure is intended to coverany and all subsequent adaptations or variations of variousimplementations. Combinations of the above implementations, and otherimplementations not specifically described herein, will be apparent tothose of skill in the art upon reviewing the description.

The Abstract of the Disclosure is submitted with the understanding thatit will not be used to interpret or limit the scope or meaning of theclaims. In addition, in the foregoing Detailed Description, variousfeatures may be grouped together or described in a single implementationfor the purpose of streamlining the disclosure. Examples described aboveillustrate but do not limit the disclosure. It should also be understoodthat numerous modifications and variations are possible in accordancewith the principles of the present disclosure. As the following claimsreflect, the claimed subject matter may be directed to less than all ofthe features of any of the disclosed examples. Accordingly, the scope ofthe disclosure is defined by the following claims and their equivalents.

What is claimed is:
 1. A device comprising: a processor configured to:receive flight data during a flight of a first aircraft; generate, basedat least in part on the flight data and a tail-specific aircraftperformance model for the first aircraft, a recommended cost index and arecommended cruise altitude; provide the recommended cost index and therecommended cruise altitude for the flight to a display device; receivea user input indicating a selected cost index; and generate, based onthe selected cost index, a control command to update an altitude, aspeed, or both, of the first aircraft.
 2. The device of claim 1, whereinthe tail-specific aircraft performance model is based at least in parton historical flight data of the first aircraft.
 3. The device of claim1, wherein the tail-specific aircraft performance model is based atleast in part on a nominal aircraft performance model associated with asecond aircraft of the same aircraft type as the first aircraft.
 4. Thedevice of claim 1, wherein the flight data is received during the flightvia a network interface from a databus of the first aircraft.
 5. Thedevice of claim 1, wherein operation of the first aircraft based on therecommended cost index and the recommended cruise altitude balancestime-related costs and fuel-related costs to reduce overall operatingcost of the first aircraft relative to operation of the first aircraftbased on a target cost index.
 6. The device of claim 1, wherein theprocessor is further configured to determine an implicit cost index of aflight, wherein the recommended cost index is based on the implicit costindex, and wherein the implicit cost index is based on a reported grossweight of the first aircraft during the flight, a detected Mach numberof the first aircraft during the flight, a detected ground speed of thefirst aircraft during the flight, a detected airspeed of the firstaircraft during the flight, a detected pressure altitude of the firstaircraft during the flight, a detected air temperature outside the firstaircraft during the flight, or a combination thereof.
 7. The device ofclaim 1, wherein the processor is further configured to determine anestimated gross weight of the first aircraft during a flight, theestimated gross weight based on a fuel weight of the first aircraftduring the flight, a reference fuel weight, an estimated fuel flowduring the flight, a high gross weight indicator for the flight, adetected pressure altitude of the first aircraft during the flight, adetected air temperature outside the first aircraft during the flight, adetected Mach number of the first aircraft during the flight, a detectedangle of attack of the first aircraft during the flight, a detectedstabilizer trim setting of the first aircraft during the flight, or acombination thereof, wherein the recommended cost index is based on theestimated gross weight.
 8. The device of claim 7, wherein the processoris further configured to determine the recommended cost index based onan estimated minimum operating cost of the first aircraft during theflight, the estimated gross weight, the detected pressure altitude, or acombination thereof.
 9. The device of claim 1, wherein the recommendedcruise altitude corresponds to a predicted minimum operating cost cruisealtitude of the first aircraft, wherein the recommended cruise altitudeis based on estimated cost indices associated with a plurality ofcruising altitudes, wherein an estimated cost index of the estimatedcost indices is associated with the predicted minimum operating costcruise altitude, and wherein the estimated cost index is based on anestimated gross weight of the first aircraft during a flight.
 10. Thedevice of claim 1, wherein the processor is integrated into the firstaircraft.
 11. The device of claim 1, wherein the processor is integratedinto a portable Electronic Flight Bag (EFB) computer.
 12. The device ofclaim 1, wherein the processor is further configured to generate thetail-specific aircraft performance model by modifying a nominal aircraftperformance model based on historical flight data of the first aircraft,wherein the nominal aircraft performance model is associated with asecond aircraft of the same aircraft type as the first aircraft.
 13. Thedevice of claim 12, wherein the nominal aircraft performance model isbased on second flight data of the second aircraft, and wherein theprocessor is configured to determine whether to modify the nominalaircraft performance model based on the historical flight data of thefirst aircraft based on determining whether the historical flight datasatisfies a filter criterion.
 14. The device of claim 13, wherein theprocessor is further configured, prior to determining whether thehistorical flight data satisfies the filter criterion, sort thehistorical flight data based on a difference between a pressure altitudeof the first aircraft and a target pressure altitude, and wherein thehistorical flight data indicates the pressure altitude of the firstaircraft.
 15. The device of claim 13, wherein the processor is furtherconfigured to determine whether the historical flight data satisfies thefilter criterion based on a comparison of an angle of attack changethreshold and a difference between a first angle of attack associatedwith the first aircraft and a second angle of attack associated with thefirst aircraft, wherein the historical flight data indicates the firstangle of attack and the second angle of attack, wherein the first angleof attack is associated with a first data collection time, and whereinthe second angle of attack is associated with a second data collectiontime.
 16. The device of claim 13, wherein the processor is furtherconfigured to: determine an average fuel mileage based on the historicalflight data; and determine whether the historical flight data satisfiesthe filter criterion based on a comparison of a fuel mileage indicatedby the historical flight data and a fuel mileage threshold, wherein thefuel mileage threshold is based on the average fuel mileage.
 17. Amethod comprising: receiving, by a device, flight data during a flightof a first aircraft; generating, based at least in part on the flightdata and a tail-specific aircraft performance model for the firstaircraft, a recommended cost index and a recommended cruise altitude;providing the recommended cost index and the recommended cruise altitudefor the flight from the device to a display device of the firstaircraft; receiving a user input indicating a selected cost index; andgenerating, based on the selected cost index, a control command toupdate an altitude, a speed, or both, of the first aircraft.
 18. Themethod of claim 17, further comprising updating the tail-specificaircraft performance model based on the flight data.
 19. Anon-transitory computer-readable medium storing instructions that, whenexecuted by a processor, cause the processor to: receive flight dataduring a flight of a first aircraft; generate, based at least in part onthe flight data and a tail-specific aircraft performance model for thefirst aircraft, a recommended cost index and a recommended cruisealtitude; provide the recommended cost index and the recommended cruisealtitude for the flight to a display device of the first aircraft;receive a user input indicating a selected cost index; and generate,based on the selected cost index, a control command to update analtitude, a speed, or both, of the first aircraft.
 20. Thenon-transitory computer-readable medium of claim 19, wherein theinstructions, when executed by the processor, also cause the processorto, in response to determining that the flight data satisfies a filtercriterion, update tail-specific aircraft performance model based on theflight data.